A cloud computing infrastructure hosts a web service with customer accounts. In a customer account, files of the customer account are listed in an index. Files indicated in the index are arranged in groups, with files in each group being scanned using scanning serverless functions in the customer account. The files in the customer account include a compressed tar archive of a software container. Member files of a compressed tar archive in a customer account are randomly-accessed by way of locators that indicate a tar offset, a logical offset, and a decompressor state for a corresponding member file. A member file is accessed by seeking to the tar offset in the compressed tar archive, restoring a decompressor to the decompressor state, decompressing the compressed tar archive using the decompressor, and moving to the logical offset in the decompressed data in memory.
A Structured Query Language (SQL) injection detector extracts metadata of an SQL query to be submitted to an SQL database and identifies a related database schema of the SQL database. The metadata and the database schema are included in a prompt that is input to a Large Language Model (LLM), which generates an output that is responsive to the prompt. A response action is performed responsive to detecting from the output of the LLM that the SQL query is indicative of an SQL injection attack.
A version control platform has repositories for storing files of software development projects. A repository network graph is created, with nodes of the repository network graph representing repositories and edges between nodes representing access to repositories by the same user during a sampling time period. Communities in the repository network graph are detected using a community detection algorithm. Repositories represented by nodes in outlier communities are monitored for anomalous access.
Interactable objects of a virtual world are scanned to determine if they are malicious. A spatial resource identifier (SRI) of an interactable object is received in a virtual reality (VR) interface that is employed by a user to access the virtual world. The interactable object is scanned when it has been rendered and is in range of the user. The scanning includes querying a backend system, using the SRI, for a reputation of the interactable object when the VR interface is incapable of scanning the interactable object. Application programming interfaces (APIs) chains are scanned as packaged with interactable objects or separately from interactable objects.
G06F 21/56 - Computer malware detection or handling, e.g. anti-virus arrangements
G06F 21/54 - Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems during program execution, e.g. stack integrity, buffer overflow or preventing unwanted data erasure by adding security routines or objects to programs
G06F 21/55 - Detecting local intrusion or implementing counter-measures
5.
Identifying malicious contents that are stored in distributed hash table networks
Method and system for evaluating contents stored in a Distributed Hash Table (DHT) network are described. Contents are stored as chunks across nodes of the DHT network. Contents are subjected to cybersecurity evaluations to generate risk histories of the chunks. A reputation of a target content is determined based on corresponding risk histories of individual chunks that are present in the target content.
An enterprise network has network assets, with each network asset having a network interface. A network graph has the network assets as nodes and connections between network interfaces of network assets as edges. An activity graph has nodes and edges, with each node representing a logical resource that performs an activity on the enterprise network, and each edge representing a relationship between the logical resources. Subgraphs of the activity graph are aligned to subgraphs of the network graph to create a mapping based on network assets associated with activities. Activity subgraphs that are aligned to the same network subgraph are compared for similarity to detect anomalous activities. The network graph is displayed at different hierarchical levels as a visualization on a display screen, with risk assessments overlayed on corresponding nodes on the visualization.
A computer network of an organization has network assets and honeypots. Probes are deployed on the computer network to collect telemetry data of the network assets. Asset profiles of the network assets are extracted from the telemetry data to obtain organization-specific data. A prompt is generated, with the prompt including an instruction to generate a honeypot configuration based on the organization-specific data. The prompt is input to a generative artificial intelligence (AI) model, such as a large language model (LLM). A honeypot is configured in accordance with the honeypot configuration that is output by the generative AI model responsive to the prompt.
Novel vulnerabilities in Open-Source Software (OSS) packages are identified from comments made on repositories of a version control platform. Security-related comments are identified and converted into a conversation format, such as a dialog. A prompt that includes the dialog is created and input to a generative Artificial Intelligence (AI) model. The prompt includes instructions that guide the AI model in generating an output. The output indicates whether a component of an OSS package has a vulnerability.
G06F 21/00 - Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
G06F 21/57 - Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
9.
Controlling deployment of serverless function codes in customer accounts on serverless platforms
A customer account on a serverless platform is monitored for deployment of serverless function codes. When a serverless function code is detected to have been deployed in the customer account, the deployment is checked against admission control policies of the customer account. A corresponding serverless function in the customer account that results from the deployment of the serverless function code is terminated when the deployment of the serverless function code is prohibited by an admission control policy.
G06F 21/55 - Detecting local intrusion or implementing counter-measures
G06F 21/56 - Computer malware detection or handling, e.g. anti-virus arrangements
G06F 21/57 - Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
Disclosed are a system and methods for detecting anomalies in a computer network. Files that are stored in the computer network of an organization are enumerated. The locality-sensitive hash values of the enumerated files are calculated. Sensitive files among the enumerated files are identified based on the locality-sensitive hash values of the enumerated files. Similarity between identified sensitive files is determined based on their distance from each other. The identified sensitive files are linked on a timeline based on their similarity and creation time. One or more event filters are applied to the timeline to detect anomalous file events. Files on the timeline that are involved in the anomalous file events and users that operated on the files are flagged. Mitigation is performed on the flagged files and users.
A sandbox module on a host computer executes sample programs and includes a dynamic analysis module that collects run-time behaviors of the sample program. A static analysis module collects static behaviors of the sample programs. The behaviors are sent to a recurrent neural network to train one or more models. Each model is trained on a type of sample file. During prediction, the sequence network inputs the behaviors from an unknown sample program. An encoder encodes the behaviors into numerical tuples. A recurrent neural network model inputs the tuples and outputs a predicted behavior of the sample program based upon the tuples. A decoder decodes a numerical tuple representing the predicted behavior and outputs the predicted behavior into a decision engine. The decision engine of the sandbox outputs a decision regarding whether the sample program is malicious or not. Look up tables permit mapping between tuples and the textual behaviors.
G06F 21/53 - Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems during program execution, e.g. stack integrity, buffer overflow or preventing unwanted data erasure by executing in a restricted environment, e.g. sandbox or secure virtual machine
G06F 21/56 - Computer malware detection or handling, e.g. anti-virus arrangements
12.
Multi-model detection of malicious URL using its network
A URL is input into a virtual browser of a crawler which requests that URL and all of its resource URLs in order to form a network graph. Network features are extracted. An identity stage uses an identity model based upon lexical and host features to determine if the input URL and resource URLs are malicious or benign. A similarity stage uses a similarity model and similarity functions to produce a similarity score between each pair of the input URL and a resource URL. An activity stage uses an activity model to determine if the input URL's network is malicious or benign based upon the network graph and extracted network activity features. Edges of the graph are weights, similarity scores, attributes or network features. A finality stage concludes malicious if the identity stage concluded that the URL was malicious or the activity stage concluded that the URL's network was malicious.
G06F 16/955 - Retrieval from the web using information identifiers, e.g. uniform resource locators [URL]
H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system
H04L 67/02 - Protocols based on web technology, e.g. hypertext transfer protocol [HTTP]
Online content is received in a user computer over the public Internet. A target text of the online content is selected to check for misinformation. A search query is generated using the target text. An online search is performed using the search query to generate a first set of search results. Another online search is performed using an opposite search query, which conveys a meaning that disagrees with the target text, to generate a second set of search results. Misinformation in the online content is detected based on how much the target text aligns with collected online contents from the first and second search results.
Penetration testing is performed on a cellular network environment. An attacker system sends a request packet to a target user equipment that is connected to a base station of a cellular network. A source Internet Protocol (IP) address of the request packet is changed to an IP address that allows a response packet from the target user equipment to be received at a relay system. The request packet is encapsulated in a General Packet Radio Services Tunneling Protocol User (GTP-U) tunnel packet that is sent to a UPF of the cellular network. The response packet, which is responsive to the request packet, is received at the relay system by way of the UPF. A destination IP address of the response packet is changed to an IP address of the attacker system before forwarding the response packet to the attacker system.
Disclosed is a scam checker for checking contents for scams. The scam checker includes several artificial intelligence (AI) models to decompose an input content, generate a plan for collecting evidence for determining whether the input content is indicative of a scam, collect the evidence, and evaluate the collected evidence to generate a summary that indicates whether the input content is indicative of a scam.
Disclosed is a system and method of black box testing a cybersecurity system. An attack chain or an element of the attack chain is decomposed into constituent primitives. Primitive codes for the primitives are generated by a generative artificial intelligence (AI) model. The primitive codes are assembled into a pseudo-malware. A malware scanning engine is black box tested against the pseudo-malware to determine whether the malware scanning engine can detect the pseudo-malware.
A client locates a server in a computer network without using the Domain Name System (DNS). The client transmits a request User Datagram Protocol (UDP) packet that includes a locating pattern. Responsive to detecting the locating pattern in the request UDP packet, a monitor that monitors communication of the client informs the server of the Internet Protocol (IP) address and port number of the client. Responsive to receiving the IP address and port number of the client, the server sends the client a response UDP packet that includes the locating pattern and the IP address and port number of the server. The client uses the IP address and port number of the server from the response UDP packet to communicate with the server.
A suspicious Uniform Resource Locator (URL) of a resource on the public Internet is detected by searching a part of the URL for a candidate substring, which is the longest possible substring that is in accordance with a Base64 encoding scheme. The candidate substring is converted to a candidate binary data in accordance with the Base64 encoding scheme. The candidate binary data is then converted to a candidate American Standard Code for Information Interchange (ASCII) string. The candidate ASCII string is evaluated to determine the information type of the candidate ASCII string. A determination as to whether the URL is suspicious is based at least on the information type of the candidate ASCII string.
G06F 16/955 - Retrieval from the web using information identifiers, e.g. uniform resource locators [URL]
H03M 7/46 - Conversion to or from run-length codes, i.e. by representing the number of consecutive digits, or groups of digits, of the same kind by a code word and a digit indicative of that kind
Bring Your Own Vulnerable Driver (BYOVD) attacks are detected in Windows-based desktop computers. An opening of a device handle to a device object by a process is detected in the desktop computer. An object path that is used in the opening of the device handle is obtained and parsed to identify a device name of the device object. The device name is compared to device names associated with drivers that have known vulnerabilities. The process that opened the device handle is detected as indicative of a BYOVD attack if the device name is associated with a driver that has a known vulnerability and the process is not a normal process.
A system for preventing submission of sensitive information of an enterprise to a generative artificial intelligence (AI) chatbot includes an endpoint computer and a backend system. A user of the enterprise employs the endpoint computer to chat with the generative AI chatbot. The chat is monitored on the endpoint computer, and a record of the chat is provided to the backend system over the public Internet. In the backend system, the record of the chat is parsed to identify the enterprise's sensitive information, enabling the facilitation of corresponding security management responses and actions.
Interactable objects of a virtual world are scanned to determine if they are malicious. A spatial resource identifier (SRI) of an interactable object is received in a virtual reality (VR) interface that is employed by a user to access the virtual world. The interactable object is scanned when it has been rendered and is in range of the user. The scanning includes querying a backend system, using the SRI, for a reputation of the interactable object when the VR interface is incapable of scanning the interactable object.
Vulnerable cloud resource dependencies are identified in codebases. A codebase is scanned for references to one or more cloud resources. Untrusted cloud resources are identified by comparing the referenced cloud resources to an inventory of trusted cloud resources. An untrusted cloud resource is detected to be vulnerable to a cyberattack in response to determining that a subdomain of the untrusted cloud resource cannot be resolved to an Internet Protocol (IP) address by a Domain Name System (DNS) server and can be registered with a cloud service provider.
Filters that include matching criteria for detecting data indicative of attack techniques of cyber threats are provided in a repository. Filters that meet filter conditions of a rule of a heuristic model are automatically included in the rule. Filters that have been automatically included in the rule by having met the filter conditions of the rule are automatically removed from the rule when the filters no longer meet the filter conditions of the rule. A security log is scanned for data that meet matching criteria of filters included in the rule. The heuristic model issues an alert at least in response to detecting that the security log includes data that meet matching criteria of filters included in the rule.
Network threat events are declared in response to detecting network traffic data indicative of network threats in network traffic involving hosts of a private computer network. Common hosts of the private computer network are identified in network threat events that have occurred within a sampling period. For each identified common host, a baseline of network behavior of the common host in network threat events that have occurred within a sliding time window is generated. A new threat event that has occurred after the sliding time window is identified as anomalous by comparing a network behavior of a common host in the new network threat event against the baseline of network behavior of the common host. An alert is issued in response to detecting an anomalous network threat event that has a risk rating that exceeds a threshold risk level.
Anomalous loading of a dynamic-link library (DLL) is detected on a desktop computer. When the file of the DLL is not a known normal file, the locality sensitive (LSH) values of files of known versions of a known normal DLL that corresponds to the DLL are obtained from an LSH repository and compared to the LSH value of the file of the DLL. The smallest distance between the LSH value of the file of the DLL and each of the LSH values from the repository is selected for comparison to a risk threshold. The Loading of the DLL is detected to be an anomaly when the smallest distance is not less than the risk threshold.
A cloud computing platform provides compute instances of customer accounts. A shell script in a compute instance is evaluated by normalizing the shell script to output a set of tokens that are separated by a predetermined separator. The set of tokens is searched for presence of reference tokens. The counts of the reference tokens in the set of tokens are formed into an occurrence vector, which is evaluated by a machine learning model to determine if the shell script is malicious.
Software products that are installed on a computer are identified from application names of application programs on the computer. The application names are pre-filtered to discard those that do not meet initial product filtering criteria that were used to collect sample data from which training datasets for training encoder-only transformer models were selected. Application names that meet initial product filtering criteria are classified using the encoder-only transformer models. Application names that have been classified by the encoder-only transformer models as those of supported products are post-filtered to discard those that do not meet the initial product filtering criteria of corresponding supported products. Application names that have not been discarded by the post-filtering are deemed to be those of software products installed on the computer.
G06F 21/00 - Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
G06F 21/57 - Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
Disclosed are a system and method for detecting an unauthorized Internet of things (IoT) device in a private computer network. Packets of network traffic are collected in the private computer network. An IoT packet is identified from among the collected packets. IoT data is extracted from the IoT packet and encrypted. The encrypted IoT data is transmitted to an artificial neural network in the cloud over the public Internet. The artificial neural network infers, based on the encrypted IoT data, a device type of an IoT device that transmitted the IoT packet in the private computer network. The IoT device is detected to be unauthorized in response to detecting that the device type of the IoT device is not a recognized IoT device type in the private computer network.
H04L 41/0604 - Management of faults, events, alarms or notifications using filtering, e.g. reduction of information by using priority, element types, position or time
H04L 41/22 - Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks comprising specially adapted graphical user interfaces [GUI]
A cybersecurity system protects personally identifiable information (PII) of users on a computer network. A webpage received in an endpoint computer of the cybersecurity system is parsed to detect one or more input fields that ask a user of the endpoint computer for a PII. Instead of entering the PII of the user into an input field, a substitute value for the PII of the user is entered into the input field based at least on a website that served the webpage to the endpoint computer.
A system for protecting public-facing computing assets of an organization includes a correlation system and security appliances. Public-facing computing assets of the organization are discovered as being accessible from the Internet. The security appliances monitor network traffic between monitored computing assets of the organization and clients on the Internet. The correlation system correlates certificate information of digital certificates of the monitored computing assets with certificate information of digital certificates of the discovered public-facing computing assets to identify an unprotected computing asset.
A secure web gateway is deployed on the cloud between a web client and a web server. The secure web gateway sends the web client a redirect response status code with a replacement server location in response to a Hypertext Transfer Protocol (HTTP) request sent by the web client to access a target resource on the web server. The secure web gateway thereafter receives from the web client a Hypertext Transfer Protocol Secure (HTTPS) request to access the target resource, the HTTPS request includes the replacement server location. The secure web gateway sends the HTTPS request as an HTTP request to the web server. The secure web gateway receives an HTTP response from the web server, and forwards the HTTP response as an HTTPS response to the web client.
Similar geographically proximate infrastructures are identified from a received compromised Internet protocol (IP) address of a compromised infrastructure. The geographic location of the compromised infrastructure is determined from the compromised IP address. The geographic locations of other infrastructures are determined from their respective exposed IP addresses. Geographically proximate infrastructures are identified from among the other infrastructures, with the geographically proximate infrastructures having geographic locations that are within a predetermined distance of the geographic location of the compromised infrastructure. Similar geographically proximate infrastructures are identified from among the geographically proximate infrastructures, with the similar geographically proximate infrastructures having a same industrial purpose as the compromised infrastructure.
Behavior report generation monitors the behavior of unknown sample files executing in a sandbox. Behaviors are encoded and feature vectors created based upon a q-gram for each sample. Prototypes extraction includes extracting prototypes from the training set of feature vectors using a clustering algorithm. Once prototypes are identified in this training process, the prototypes with unknown labels are reviewed by domain experts who add a label to each prototype. A K-Nearest Neighbor Graph is used to merge prototypes into fewer prototypes without using a fixed distance threshold and then assigning a malware family name to each remaining prototype. An input unknown sample can be classified using the remaining prototypes and using a fixed distance. For the case that no such prototype is close enough, the behavior report of a sample is rejected and tagged as an unknown sample or that of an emerging malware family.
G06F 18/23213 - Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
34.
System and method for evaluating performance of a patching process of an enterprise network
An enterprise network has endpoints, which are computers with a computer program that needs patches to remove vulnerabilities. A plot of a percentage of vulnerable endpoints over time is generated. Patching cycles and residual phases are identified in the plot. A Residual Vulnerable Percentage (RVP) is determined from the plot, the RVP being an average of percentage of vulnerable endpoints in a residual phase. A Time to Patch Managed (TTPM) is determined from the plot as a time period from a beginning of a patching cycle to a beginning of a residual phase in the patching cycle. A performance indicator that is based on the RVP or the TTPM is compared to a corresponding reference to determine if a corrective action needs to be performed to address deficiencies in the efficiency and/or effectiveness of the patching process.
A cloud computing infrastructure hosts a web service with customer accounts. In a customer account, files of the customer account are listed in an index. Files indicated in the index are arranged in groups, with files in each group being scanned using scanning serverless functions in the customer account. The files in the customer account include a compressed tar archive of a software container. Member files of a compressed tar archive in a customer account are randomly-accessed by way of locators that indicate a tar offset, a logical offset, and a decompressor state for a corresponding member file. A member file is accessed by seeking to the tar offset in the compressed tar archive, restoring a decompressor to the decompressor state, decompressing the compressed tar archive using the decompressor, and moving to the logical offset in the decompressed data in memory.
System and method for taking actions to mitigate security risks in a computer network are disclosed. The costs of security events and taking actions to block permissions granted to users are defined. For each of the users, prediction models are created, one for each of the security events. Using prediction models of a selected user, predictions on whether the security events will occur and/or predictions of severity if the security events actually occur are generated. For the selected user, an action list that indicates whether or not to take actions to block particular permissions granted to the selected user is generated based at least on the predictions, costs of the events, and costs of the permissions.
An endpoint computer is protected from malicious distributed configuration profiles. The endpoint computer receives a distributed configuration profile over a computer network. Before installation of the distributed configuration profile in the endpoint computer, features of the distributed configuration profile are used to traverse a supervised decision tree. A rating score is generated based on weights of nodes of the supervised decision tree that are traversed using the features of the distributed configuration profile. The distributed configuration profile is detected to be malicious based at least on the rating score.
G06F 40/143 - Markup, e.g. Standard Generalized Markup Language [SGML] or Document Type Definition [DTD]
H04N 21/8543 - Content authoring using a description language, e.g. MHEG [Multimedia and Hypermedia information coding Expert Group] or XML [eXtensible Markup Language]
38.
Detecting an operational state of antivirus software
Proper functioning of an antivirus software running on an endpoint system is detected using a test data that is provided to the endpoint system. The test data is also provided to a backend system, which provides the endpoint system with an antivirus definition that includes information for detecting the test data. The antivirus software running on the endpoint system scans for the test data and reports detection of the test data to the backend system. The antivirus software is deemed to have failed the proper functioning test when the antivirus software fails to report detection of the test data. Proper functioning of the antivirus software is also detected by performing a challenge procedure, which involves sending a challenge message to the endpoint system. The endpoint system is expected to respond to the challenge message with a response that includes expected information.
G06F 21/00 - Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
G06F 21/56 - Computer malware detection or handling, e.g. anti-virus arrangements
G06F 21/57 - Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
39.
Privacy protection for customer events logs of cybersecurity events
System and methods of analyzing customer events logs for cybersecurity with privacy protection are disclosed. Events logs of cybersecurity events are received from customer computers. Customers in the events logs are represented with ring signatures. Candidate features that occur in a group of events are identified in the events logs. A candidate feature is analyzed, based on corresponding ring signatures, to determine if the candidate feature can be attributed to a customer or a limited number of customers. If so, the candidate feature is considered private and is discarded. Otherwise, the candidate feature is retained as public data suitable for use in cybersecurity operations.
G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system
A computer system generates a hierarchical evolutionary tree of digests of sample files. The digests are generated using a locality sensitive hashing function. The digests are grouped into several clusters, and the clusters are grouped into several nodes. The nodes are connected in hierarchical order to generate the hierarchical evolutionary tree. A digest of a file being evaluated for malware is generated using the locality sensitive hashing function. The digest is put in a cluster of the hierarchical evolutionary tree having digests that are most similar to the digest relative to digests of other clusters of the hierarchical evolutionary tree. The digest is identified to be of the same malware family as the digests of the cluster.
H04L 9/06 - Arrangements for secret or secure communicationsNetwork security protocols the encryption apparatus using shift registers or memories for blockwise coding, e.g. D.E.S. systems
41.
Detecting network entities that pose a cybersecurity risk to a private computer network
A risk knowledge graph is created from information on risk events involving network entities of a private computer network. Each of the risk events is represented as a node in the risk knowledge graph. The nodes are connected by edges that represent the risk events. The nodes are grouped into communities of related nodes. A response action is performed against a community to mitigate a cybersecurity risk posed by the community.
A cloud computing infrastructure hosts a web service with customer accounts. In a customer account, files of the customer account are listed in an index. Files indicated in the index are arranged in groups, with files in each group being scanned using scanning serverless functions in the customer account. The files in the customer account include a compressed tar archive of a software container. Member files of a compressed tar archive in a customer account are randomly-accessed by way of locators that indicate a tar offset, a logical offset, and a decompressor state for a corresponding member file. A member file is accessed by seeking to the tar offset in the compressed tar archive, restoring a decompressor to the decompressor state, decompressing the compressed tar archive using the decompressor, and moving to the logical offset in the decompressed data.
Anomalous activities on a computer network are detected from audit or sign-in activity information of a target entity as recorded in an audit or sign-in log. A baseline graph of the target entity is generated using information on activities of the target entity during a collection period. A predict graph of the target entity is generated with information on activities of the target entity during another collection period, which follows and is shorter than the earlier collection period. A residual graph that indicates nodes or edges that are in the predict graph but not in the baseline graph is generated. The residual graph is scored and the score is compared to a threshold to determine whether the target entity has performed an anomalous activity.
Systems and methods for Internet access control are presented. A third-party application is hosted by a third-party server on the Internet. The third-party application has third-party data of a user. An Internet access control device detects an Internet access by the user to a target server on the Internet. The Internet access control device allows or blocks the Internet access depending on whether the Internet access is permitted or prohibited based on the third-party data.
Behavior report generation monitors the behavior of unknown sample files executing in a sandbox. Behaviors are encoded and feature vectors created based upon a q-gram for each sample. Prototypes extraction includes extracting prototypes from the training set of feature vectors using a clustering algorithm. Once prototypes are identified in this training process, the prototypes with unknown labels are reviewed by domain experts who add a label to each prototype. A K-Nearest Neighbor Graph is used to merge prototypes into fewer prototypes without using a fixed distance threshold and then assigning a malware family name to each remaining prototype. An input unknown sample can be classified using the remaining prototypes and using a fixed distance. For the case that no such prototype is close enough, the behavior report of a sample is rejected and tagged as an unknown sample or that of an emerging malware family.
G06F 18/23213 - Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
46.
Automated mitigation of cyber threats using a semantic cybersecurity database
Systems and methods are presented for mitigating cyber threats. Cybersecurity-related data are stored in a semantic cybersecurity database. A user interface converts a user input to a command utterance. A command node that corresponds to the command utterance is identified in the cybersecurity database. The command node is resolved to one or more action nodes that are connected to the command node, and each action node is resolved to one or more parameter nodes that are connected to the action node. The command node has a command that implements actions indicated in the action nodes. Each action can have one or more required parameters indicated in the parameter nodes. The values of the required parameters are obtained from the command utterance, prompted from the user, or obtained from the cybersecurity database. Actions with their parameter values are executed to mitigate a cyber threat in accordance with the user input.
A method for preventing spam comments from populating a web site is provided. The method includes intercepting a HTTP (Hypertext Transfer Protocol) response, which includes a web page with a form for enabling a client's general comments to be published on the web site. The method also includes modifying the web page with the form to create a modified web page with a randomized form. The modifying includes randomly adding a set of randomized variable names to the web page with the form. The set of randomized variable names is a set of randomly generated character strings. The method further includes forwarding the modified web page with the randomized form to the client. The method yet also includes adding the set of randomized variable name to a form database, which is configured for storing data about the modified web page with the randomized form.
G06F 16/958 - Organisation or management of web site content, e.g. publishing, maintaining pages or automatic linking
G06F 21/54 - Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems during program execution, e.g. stack integrity, buffer overflow or preventing unwanted data erasure by adding security routines or objects to programs
G06F 21/36 - User authentication by graphic or iconic representation
H04L 51/52 - User-to-user messaging in packet-switching networks, transmitted according to store-and-forward or real-time protocols, e.g. e-mail for supporting social networking services
48.
Systems and methods for preventing information leakage
A system for preventing information leakage due to access by an application to a file is provided. The system for preventing information leakage includes an application identification module configured to obtain data associated with the application. The system for preventing information leakage also includes an association table containing file-type data and trusted-application data. In addition, the system also includes an access control module to determine the application identifier and the association table. The system for preventing information leakage is configured to determine whether to deny content access by the application to content of the file as saved in the file.
Features of sample files that are known to be normal are extracted by random projection. The random projection values of the sample files are used as training data to generate one or more anomaly detection models. Features of a target file being inspected are extracted by generating a random projection value of the target file. The random projection value of the target file is input to an anomaly detection model to determine whether or not the target file has features that are novel relative to the sample files. The target file is declared to be an outlier when an anomaly detection model generates an inference that indicates that the target file has novel features.
Methods and apparatus for detecting, in a gateway device configured for facilitating communication between an intranet and an external network, the existence of an unauthorized wireless access point in the intranet. The method includes determining whether a packet received at the gateway originates from one of the wireless devices. If a received at the gateway originates from a wireless device, the method includes determining whether a source MAC address associated with the packet is one of the set of known MAC addresses. If not, the method further includes taking a remedial action to prevent the unauthorized wireless access point from accessing one of the intranet and the external network.
A multiclass classifier generates a probability vector for individual data units of an input data stream. The probability vector has prediction probability values for classes that the multiclass classifier has been trained to detect. A class with the highest prediction probability value among the classes in a probability vector is selected as the predicted class. A confidence score is calculated based on the prediction probability value of the class. Confidence scores of the class are accumulated within a sliding window. The class is declared to be the detected class of the input data stream when the accumulated value of the class meets an accumulator threshold. A security policy for an application program that is mapped to the class is enforced against the input data stream.
A computer network includes a camera node, a network access node, a verification node, and a display node. Video content recorded by a camera at the camera node is transmitted to the display node and to the verification node for verification. The video content is verified at the display node and at the verification node. Recording metadata of the video content is stored in a distributed ledger and retrieved by the display node to verify the video content. The verification node receives, from the network access node, verification data for verifying the video content.
G06F 16/787 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using geographical or spatial information, e.g. location
G06F 16/783 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
A scam detection system includes a user computer that runs a security application and a backend system that runs a scam detector. An email is received at the user computer. The security application extracts and forwards a content of the email, which includes a body of the email, to the backend system. The email body of the email is anonymized by removing personally identifiable information from the email body. A hash of the anonymized email body is generated and compared against hashes of a whitelist and a blacklist. The anonymized email body is classified. A segment of text of the anonymized email body is identified and provided to the user computer when the anonymized email body is classified as scam.
A system for stateful detection of cyberattacks includes an endpoint computer and a backend computer system. The endpoint computer monitors for cyberattacks and sends out queries for detected security events. The backend computer system stores observation data that are included in the queries. The backend computer system combines current observation data from a current query, relevant observation data from previous queries, and relevant cybersecurity data. The combined data are evaluated for cyberattacks.
A login authentication process to access a computer service includes displaying a virtual keyboard on a display screen of a computer. A user enters a password by clicking on the virtual keyboard. The manner the user clicked on the virtual keyboard to enter the password is compared to the manner an authorized user of the computer service clicked on the virtual keyboard to enter an authorized password during a learning phase. The login authentication is deemed to be a success when the password matches the authorized password, and the manner the user clicked on the virtual keyboard to enter the password matches the manner the authorized user clicked on the virtual keyboard to enter the authorized password.
G06F 3/048 - Interaction techniques based on graphical user interfaces [GUI]
G06F 21/36 - User authentication by graphic or iconic representation
G06F 3/0354 - Pointing devices displaced or positioned by the userAccessories therefor with detection of 2D relative movements between the device, or an operating part thereof, and a plane or surface, e.g. 2D mice, trackballs, pens or pucks
G06F 3/0482 - Interaction with lists of selectable items, e.g. menus
G06F 3/04886 - Interaction techniques based on graphical user interfaces [GUI] using specific features provided by the input device, e.g. functions controlled by the rotation of a mouse with dual sensing arrangements, or of the nature of the input device, e.g. tap gestures based on pressure sensed by a digitiser using a touch-screen or digitiser, e.g. input of commands through traced gestures by partitioning the display area of the touch-screen or the surface of the digitising tablet into independently controllable areas, e.g. virtual keyboards or menus
G06F 3/04815 - Interaction with a metaphor-based environment or interaction object displayed as three-dimensional, e.g. changing the user viewpoint with respect to the environment or object
A system for facilitating Internet security for devices on a local area network (LAN) is disclosed. The LAN may connect to a rating server through the Internet and may including at least an anti-malware application for detecting malware. The system may include a black list for being implemented on the LAN for storing identifiers of a set of forbidden sites. The devices may be prevented from accessing content provided by each of the forbidden sites. The system may also include a profiler for being implemented on the LAN for updating the black list utilizing a set of result data. The data may include scan result data and rating result data. The scan result data may pertain to results of scans performed by the anti-malware application; the rating result data may pertain to results of rating performed by the rating server.
A target binary file is clustered by reducing the target binary file to its architecture-agnostic functions, which are converted into an input string. The target digest of the input string is calculated and compared to digests of malicious binary files. A cluster having digests of malicious binary files that are similar to the target digest is identified. In response to identifying the cluster, the target binary file is detected to be malicious and of the same malware family as the malicious binary files of the cluster.
An endpoint system receives a target file for evaluation for malicious scripts. The original content of the target file is normalized and stored in a normalized buffer. Tokens in the normalized buffer are translated to symbols, which are stored in a tokenized buffer. Strings in the normalized buffer are stored in a string buffer. Tokens that are indicative of syntactical structure of the normalized content are extracted from the normalized buffer and stored in a structure buffer. The content of the tokenized buffer and counts of tokens represented as symbols in the tokenized buffer are compared against heuristic rules indicative of malicious scripts. The contents of the tokenized buffer and string buffer are compared against signatures of malicious scripts. The contents of the tokenized buffer, string buffer, and structure buffer are input to a machine learning model that has been trained to detect malicious scripts.
A file is stored in a public cloud storage. A serverless computing platform receives an event notification that the file has been stored and, in response, creates an instance of an ephemeral environment wherein a security module is executed. The security module creates a memory-mapped space with memory locations that are mapped to the entire content of the file but does not allocate memory for all of the memory locations. Instead, the security module retrieves sections of the file from the public cloud storage as these sections are accessed in their designated memory locations in accordance with the memory mapping, allocates memory for the retrieved sections, stores the retrieved sections in their designated memory locations, and scans the retrieved sections in their designated memory locations for malicious code. The security module continues scanning the file in sections until relevant sections of the file have been scanned.
A machine learning system includes multiple machine learning models. A target object, such as a file, is scanned for machine learning features. Context information of the target object, such as the type of the object and how the object was received in a computer, is employed to select a machine learning model among the multiple machine learning models. The machine learning model is also selected based on threat intelligence, such as census information of the target object. The selected machine learning model makes a prediction using machine learning features extracted from the target object. The target object is allowed or blocked depending on whether or not the prediction indicates that the target object is malicious.
A virtual keyboard rendered on a separate computing device is independent of the user's computer. A virtual keyboard displayed on the user's computer screen is blank without any alphanumeric characters. Another virtual keyboard displayed on the user's independent computing device has a randomly generated layout of alphanumeric characters on a keypad. The user enters a password by pressing the blank keys of the blank keyboard on his computer screen with reference to the other virtual keyboard. The position sequence of these entered keys is sent to an application on a remote server computer. The remote server computer shares a virtual keyboard having the randomly generated layout of characters with the independent computing device via an online or off-line technique. When online, an encoded image of the encrypted layout is sent to the client computer and displayed for scanning by the device. When off-line, both the application and the device generate the same random key sequence by using the same pseudo random number generator and the same seed value.
G06F 3/04886 - Interaction techniques based on graphical user interfaces [GUI] using specific features provided by the input device, e.g. functions controlled by the rotation of a mouse with dual sensing arrangements, or of the nature of the input device, e.g. tap gestures based on pressure sensed by a digitiser using a touch-screen or digitiser, e.g. input of commands through traced gestures by partitioning the display area of the touch-screen or the surface of the digitising tablet into independently controllable areas, e.g. virtual keyboards or menus
H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system
G06K 7/14 - Methods or arrangements for sensing record carriers by electromagnetic radiation, e.g. optical sensingMethods or arrangements for sensing record carriers by corpuscular radiation using light without selection of wavelength, e.g. sensing reflected white light
G06F 21/42 - User authentication using separate channels for security data
G06F 21/34 - User authentication involving the use of external additional devices, e.g. dongles or smart cards
62.
Decryption of encrypted network traffic using an inline network traffic monitor
An inline network traffic monitor is deployed inline between two endpoints of a computer network. A particular endpoint of the two endpoints works in conjunction with the inline network traffic monitor to decrypt encrypted network traffic transmitted between the two endpoints. A series of Change Cipher Spec (CCS) messages is exchanged between the inline network traffic monitor and the particular endpoint during a Transport Layer Security (TLS) handshake between the two endpoints. The series of CCS messages allows the particular endpoint and the inline network traffic monitor to detect each other on the computer network. After detecting each other's presence, the particular endpoint sends the inline network traffic monitor a session key that is used by the two endpoints to encrypt their network traffic. The inline network traffic monitor uses the session key to decrypt encrypted data of the network traffic transmitted between the two endpoints.
An attachment to an e-mail message received at an e-mail gateway is scanned by a scan server and then is converted into an HTML file. The HTML file includes preview data of the attachment (minus any macro scripts), the entire original data of the attachment, scan functionality enabling a user to send the attachment back to a scan server for a second scan, or extract functionality enabling a user to extract the original attachment data for saving or opening in an application. The recipient is able to open or save the attachment directly if he or she believes it comes from a trusted sender. If the attachment seems suspicious, the recipient previews the attachment first before performing a scan, opening the attachment or deleting it. The recipient performs a scan of the attachment by clicking a “scan” button to send the attachment to a backend server for a second scan where an updated virus pattern file may be available to detect any zero-day malware.
A pause command is sent to a Subscriber Identity Module (SIM) card of a cellular device in response to detecting a cyberattack against the cellular device on the cellular network. To mitigate the cyberattack, the SIM card temporarily disconnects the cellular device from the cellular network for a pause time. The SIM card prohibits the cellular device from connecting to the cellular network during the pause time and automatically allows the cellular device to reconnect to the cellular network after the pause time.
A locality-sensitive hash value is calculated for a suspect file in an endpoint computer. A similarity score is calculated for the suspect hash value by comparing it to similarly-calculated hash values in a cluster of known benign files. A suspiciousness score is calculated for the suspect hash value based upon similar matches in a cluster of benign files and a cluster of known malicious files. These similarity score and the suspiciousness score or combined in order to determine if the suspect file is malicious or not. Feature extraction and a set of features for the suspect file may be used instead of the hash value; the classes would contain sets of features rather than hash values. The clusters may reside in a cloud service database. The suspiciousness score is a modified Tarantula technique. Matching of locality-sensitive hashes may be performed by traversing tree structures of hash values.
A method protects a daemon in an operating system of a host computer. The operating system detects that there is an access of a plist file of a daemon by a process in the computer. If so, then it executes a callback function registered for the plist file. The callback function sends to a kernel extension a notification of the attempted access. The kernel extension returns a value to the operating system indicating that the access should be denied. The operating system denies access to the plist file of the daemon by the process. The extension may also notify an application which prompts the user for instruction. The kernel extension also protects itself by executing its exit function when a command is given to unload the extension, and the exit function determines whether or not the command is invoked by an authorized application, such as by checking a flag.
G06F 21/56 - Computer malware detection or handling, e.g. anti-virus arrangements
G06F 21/52 - Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems during program execution, e.g. stack integrity, buffer overflow or preventing unwanted data erasure
67.
Automatic charset and language detection with machine learning
Language-based machine learning approach for automatically detecting universal charset and the language of a received document is disclosed. The language-based machine learning approach employs a plurality of text document samples in different languages, after converting them to a selected Unicode style (if their original encoding schemes are not the selected Unicode), to generate a plurality of language-based machine learning models during the training stage. During the application stage, vector representations of the received document for different combinations of charsets and their respective applicable languages are tested against the plurality of machine learning models to ascertain the charset and language combination that is most similar to its associated machine learning model, thereby identifying the charset and language of the received document.
An automation task program is inspected for unsecure data flow. The task program is parsed to generate a parse tree, which is visited to generate control flow graphs of functions of the task program. The control flow graphs have nodes, which have domain-agnostic intermediate representations. The control flow graphs are connected to form an intermediate control flow graph. The task program is deemed to have an unsecure data flow when data is detected to flow from a data source to a data sink, with the data source and the data sink forming a source-sink pair that is indicative of an unsecure data flow.
G06F 21/57 - Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
Systems and methods are presented for performing sandboxing to detect malware. Sample files are received and activated individually in separate sandboxes in one mode of operation. In another mode of operation, sample files are assigned to pools. Sample files of a pool are activated together in the same sandbox. The sample files of the pool are deemed to be normal when no anomalous event is detected in the sandbox. Otherwise, when an anomalous event is detected in the sandbox, the sample files of the pool are activated separately in separate sandboxes to isolate and identify malware among the sample files.
G06F 21/53 - Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems during program execution, e.g. stack integrity, buffer overflow or preventing unwanted data erasure by executing in a restricted environment, e.g. sandbox or secure virtual machine
G06F 21/56 - Computer malware detection or handling, e.g. anti-virus arrangements
A mobile app is in a form of a package file. A structural feature digest is generated from contents of a manifest part, bytecode part, and resource part of the package file. A mobile device receives an unknown mobile app, generates a structural feature digest of the unknown mobile app, and sends the structural feature digests to a backend system over a computer network. In the backend system, the structural feature digest of the unknown mobile app is compared to structural feature digests of known malicious mobile apps. The unknown mobile app is detected to be malicious when its structural feature digest is similar to that of a known malicious mobile app.
An agent on an endpoint computer computes a locality-sensitive hash value for an API call sequence of an executing process. This value is sent to a cloud computer which includes an API call sequence blacklist database of locality-sensitive hash values. A search is performed using a balanced tree structure of the database using the received hash value and a match is determined based upon whether or not a metric distance is under or above a distance threshold. The received value may also be compared to a white list of locality-sensitive hash values. Attribute values of the executing process are also received from the endpoint computer and may be used to inform whether or not the executing process is deemed to be malicious. An indication of malicious or not is returned to the endpoint computer and if malicious, the process may be terminated and its subject file deleted.
Taint is dynamically tracked on a mobile device. Taint virtual instructions are added to virtual instructions of a control-flow graph (CFG). A taint virtual instruction has a taint operand that corresponds to an operand of a virtual instruction and has a taint output that corresponds to an output of the virtual instruction in a block of the CFG. Registers are allocated for the taint virtual instruction and the virtual instructions. After register allocation, the taint virtual instruction and the virtual instructions are converted to native code, which is executed to track taint on the mobile device.
A system includes Internet of things (IOT) devices that are paired with corresponding edge computers. Smart contracts are generated for edge computers, and deployed in a blockchain. Upon receipt of a message, a smart contract compares a sender of the message to a designated owner of the smart contract. The smart contract has a privilege checker that allows a message from the owner of the smart contract to initiate execution of a function that modifies a variable of the smart contract, but prevents messages from non-owners from initiating execution of the function.
H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
H04L 67/1097 - Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
The system executes online on corporate premises or in a cloud service, or offline. An e-mail message is received at a server within a corporate network or cloud service. A header of the e-mail message is parsed to determine locations of server computers through which the e-mail message has traveled. Geographic locations are placed into a routing map. A banner is inserted into the e-mail message that includes the routing map or a link to the routing map. The routing map is stored by the e-mail gateway server at a storage location identified by the link. The modified e-mail message is delivered or downloaded from the e-mail server to a user computer in real time. The sender Web site is parsed to identify sender domain information to be inserted into the banner. If offline, a product fetches and modifies the e-mail message using an API of the e-mail server.
A system is implemented in browser plug-in software or in endpoint agent software on a user computer. The user accesses a Web site and fills in a login request form and submits it to the Web site. The system triggers a “forgot password” feature and detects a phishing Web site by determining that it does not send a reset link to a valid user e-mail address, or, the system detects a phishing Web site by determining that it does send a reset link to an invalid e-mail address. Or, the system detects a phishing Web site by determining that it sends a reset link to a user e-mail address from a domain different from the domain of a login request form. Or, the system fills in an incorrect account name or password in a login request form and detects a phishing Web site by determining that the Web site does not indicate that the incorrect user name or incorrect password are incorrect. Or, the system submits incorrect credentials and detects a phishing Web site by determining that the Web site does not implement any way to reset the account name or password.
G06F 15/16 - Combinations of two or more digital computers each having at least an arithmetic unit, a program unit and a register, e.g. for a simultaneous processing of several programs
H04L 29/06 - Communication control; Communication processing characterised by a protocol
G06F 16/954 - Navigation, e.g. using categorised browsing
G06F 11/32 - Monitoring with visual indication of the functioning of the machine
76.
System and method for detecting leakage of email addresses
A system for detecting leakage of email addresses generates an alias email address that will be used by a user to register with a web service. The alias email address is an alias for a primary email address of the user, and is paired with the web service. The web service is included in a whitelist upon confirmation from the web service that the alias email address has been registered with the web service. Emails that are addressed to the alias email address and from the web service are forwarded to the primary email address. Emails that are addressed to the alias email address but is not from the web service are detected to be suspicious.
Critical network assets of a private computer are automatically identified by training a machine learning model with histograms of features obtained by aggregating data of log entries. The model is deployed in a private computer network and retrained using training data set of the private computer network. Data from log entries of a target network asset are aggregated, numerically transformed, and converted into features histograms. The features histograms are concatenated into a single file, which is provided to the machine learning model for prediction. The machine learning model outputs a prediction score that gives an indication of whether or not the target network asset is critical.
Network attacks are detected by a protocol engine that works in conjunction with one or more streaming protocol analyzers. The protocol engine receives network packets over a computer network and generates metadata of the network packets. The metadata are placed in a transport envelope, which is streamed over the computer network. The transport envelope is received over the computer network. After receiving the transport envelope over the computer network, the metadata are extracted from the transport envelope and provided to the one or more streaming protocol analyzers, which analyze the metadata to detect network attacks.
A cyber threat intelligence of a cyber threat includes a threat chain that describes objects involved in the cyber threat and relationships between the objects. A related object hash of an object is calculated by calculating a hash of one or more objects that are linked to the object as indicated in the cyber threat intelligence. A related object sequence hash of the threat chain is generated by calculating a total of the related object hashes. The related object sequence hash of the threat chain is compared to a related object sequence hash of another threat chain to detect cyber threats.
H04L 29/06 - Communication control; Communication processing characterised by a protocol
H04L 9/06 - Arrangements for secret or secure communicationsNetwork security protocols the encryption apparatus using shift registers or memories for blockwise coding, e.g. D.E.S. systems
H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system
80.
Generation of file digests for detecting malicious executable files
A cybersecurity server receives an executable file that has bytecode and metadata of the bytecode. Strings are extracted from the metadata, sorted, and merged into data streams. The data streams are merged to form a combined data stream. A digest of the combined data stream is calculated using a fuzzy hashing algorithm. The similarity of the digest to another digest is determined to detect whether or not the executable file is malware or a member of a malware family.
G06F 21/51 - Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems at application loading time, e.g. accepting, rejecting, starting or inhibiting executable software based on integrity or source reliability
G06F 21/56 - Computer malware detection or handling, e.g. anti-virus arrangements
81.
Systems and methods for distributed digital rights management with decentralized key management
One embodiment disclosed relates to a system for digital data distribution with decentralized key management. The system utilizes a data provider, a data demander, cloud storage, a blockchain, and a smart contract registered with the blockchain. The data provider encrypts the digital data using a session key and puts the encrypted digital data to the cloud storage, which returns a URL for the stored digital data. In addition, the session key is itself encrypted using the public key of the data demander. The access data at the smart contract is updated with the encrypted session key and the URL. The data demander uses its own private key to decrypt the session key and then uses the session key to decrypt the digital data. Other embodiments and features are also disclosed.
A cybersecurity system includes sensors that detect and report computer security events. Collected reports of computer security events are formed into state sequences, which are used as training data to train and build a prediction model. A current computer security event is detected and used as an input to the prediction model, which provides a prediction of a next computer security event. A monitoring level of a cybersecurity sensor is adjusted in accordance with the predicted next computer security event.
An e-mail message is sent from a public e-mail address via the e-mail account of a user and delivered to an e-mail gateway. The message is destined for the e-mail account of a recipient. The gateway determines that the public e-mail address is on a list of users desiring two-factor authentication. The gateway determines that the message contains an anomaly indicating fraud or possible forgery. The gateway sends a two-factor authentication message to a hidden e-mail account of the user. The user reviews the message and responds with a confirmation message either confirming that the message is legitimate or indicating that it is a forgery. If the message is legitimate the gateway allows the message to be delivered to the recipient; if not, the message remains in quarantine and is not delivered. The gateway exists at the user's corporation, the recipient's corporation or is hosted at a third-party cloud service.
G06F 15/16 - Combinations of two or more digital computers each having at least an arithmetic unit, a program unit and a register, e.g. for a simultaneous processing of several programs
A system for evaluating files for cyber threats includes a machine learning model and a locality sensitive hash (LSH) repository. When the machine learning model classifies a target file as normal, the system searches the LSH repository for a malicious locality sensitive hash that is similar to a target locality sensitive hash of the target file. When the machine learning model classifies the target file as malicious, the system checks if response actions are enabled for the target file. The system reevaluates files that have been declared as normal, and updates the LSH repository in the event of false negatives. The system disables response actions for files that have been reported as false positives.
A network security device has a local area network (LAN) interface and a wide area network (WAN) interface, with a capability to route packets of a network connection along a fast path that bypasses a network stack of an operating system of the network security device. A packet of a network connection that is received at the LAN interface is routed to a virtual network interface. A packet inspector reads the packet from the virtual network interface, inspects the packet, and writes the packet back to the virtual network interface after inspection. The packet is routed from the virtual network interface to the WAN interface, and exits the WAN interface towards the destination network address of the packet. After inspecting one or more packets of the network connection, subsequently received packets of the network connection are routed along the fast path.
A global locality sensitive hash (LSH) database stores global locality sensitive hashes of files of different private computer networks. Each of the private computer networks has a corresponding local LSH database that stores local locality sensitive hashes of files of the private computer network. A target locality sensitive hash is generated for a target file of a private computer network. The global and local LSH databases are searched for a locality sensitive hash that is similar to the target locality sensitive hash. The target file is marked for further evaluation for malware or other cybersecurity threats when the target locality sensitive hash is not similar to any of the global and local locality sensitive hashes.
G06F 21/53 - Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems during program execution, e.g. stack integrity, buffer overflow or preventing unwanted data erasure by executing in a restricted environment, e.g. sandbox or secure virtual machine
87.
Systems and methods for data certificate notarization utilizing bridging from private blockchain to public blockchain
One embodiment disclosed relates to a system for managing data for logistics, sourcing and/or production. The system includes: a private blockchain maintained by a first network of nodes; a trusted public blockchain maintained by a second network of nodes; a private agent system; and a bridge system connected to both the private blockchain and the public blockchain. The private agent system operates to extract blocks of metadata from the private blockchain and utilize a hash tree structure to generate a first root hash value from the blocks of metadata. The bridge system operates to verify the first root hash value and store the first root hash value as a notarized data certificate in the trusted public blockchain. Another embodiment disclosed relates to a method for data certificate notarization utilizing a bridging system from a private blockchain to a trusted public blockchain. Other embodiments and features are also disclosed.
H04L 9/06 - Arrangements for secret or secure communicationsNetwork security protocols the encryption apparatus using shift registers or memories for blockwise coding, e.g. D.E.S. systems
H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system
An attachment to an e-mail message is replaced with a URL before that message is delivered to an end user, thus providing more time to perform a better scan at a cloud server computer. The attachment is removed from the e-mail message and sent to the cloud server computer for a dynamic scan and a static scan which will likely include updates better able to detect malicious software. The e-mail message with the URL is delivered to the end user and there is a delay before the end user reads the message or attempts to open the attachment. An artificial delay may be introduced at an e-mail gateway before the message is delivered to the end-user. If the attachment is benign then the end user is allowed to download it via the URL; if the attachment is malicious then the end user is only given a warning message.
One embodiment of the presently-disclosed invention relates to an intrusion prevention system that includes a plurality of FPGA instances and a plurality of compute instances in a cloud network. The plurality of FPGA instances perform pre-processing that determines whether data packets received from the network gateway are associated with suspicious flows. The data packets associated with the suspicious flows are communicated from the plurality of FPGA instances to a plurality of compute instances in the cloud network. The plurality of compute instances perform post-processing that determines whether a suspicious flow is malicious. Other embodiments, aspects and features are also disclosed.
One embodiment disclosed relates to a system for detecting anomalous messaging, discovering compromised accounts, and generating responses to threatened attacks. The system utilizes API commands and log forwarding for interaction and communication between a messaging and account hunting platform, other hunting platforms, an action center, and a security operations center. Another embodiment relates to a method of, and system for, performing a complete root cause analysis. Another embodiment relates to a method of, and system for, anomaly discovery which may advantageously utilize reference data to correlate different anomalies for reporting as a single incident.
An intrusion prevention system includes a machine learning model for inspecting network traffic. The intrusion prevention system receives and scans the network traffic for data that match an anchor pattern. A data stream that follows the data that match the anchor pattern is extracted from the network traffic. Model features of the machine learning model are identified in the data stream. The intrusion prevention system classifies the network traffic based at least on model coefficients of the machine learning model that are identified in the data stream. The intrusion prevention system apples a network policy on the network traffic (e.g., block the network traffic) when the network traffic is classified as malicious.
A computer network includes a camera node, a network access node, a verification node, and a display node. Video content recorded by a camera at the camera node is transmitted to the display node and to the verification node for verification. The video content is verified at the display node and at the verification node. Recording metadata of the video content is stored in a distributed ledger and retrieved by the display node to verify the video content. The verification node receives, from the network access node, verification data for verifying the video content.
H04N 21/84 - Generation or processing of descriptive data, e.g. content descriptors
G06F 16/787 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using geographical or spatial information, e.g. location
G06F 16/783 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
A cybersecurity server receives an executable file. The executable file is disassembled to generate assembly code of the executable file. High-entropy blocks and blocks of printable American Standard Code for Information Interchange (ASCII) characters are removed from the assembly code. Instructions of the assembly code are normalized, chunked, and merged into a data stream. The digest of the data stream is calculated using a fuzzy hashing algorithm. The similarity of the digest to a malicious digest is determined to evaluate the executable file for malware.
In one embodiment, a network security device monitors network communications between a computer and another computer. A periodicity of transmissions made by one computer to the other computer is determined, with the periodicity being used to identify candidate time point pairs having intervals that match the periodicity. A graph is constructed with time points of the candidate time point pairs as nodes and with intervals of time point pairs as edges. A longest path that continuously links one time point to another time point on the graph is compared to a threshold length to verify that the transmissions are periodic, and are thus potentially indicative of malicious network communications.
A network device has a Local Area Network (LAN) port and several Wide Area Network (WAN) ports. The network device detects a computing device that is connected to the LAN port initiating establishment of a TCP connection. The network device creates a TCP socket that establishes the TCP connection with the computing device and inspects TCP packets on the TCP connection to identify a cloud application associated with the TCP packets. The network device creates another TCP socket that establishes a TCP connection to the identified cloud application by way of a WAN port that is designated to be an output port for the identified cloud application. A routing path is created between the LAN port and the designated WAN port. Subsequent TCP packets originated by the computing device for the identified cloud application are forwarded along the routing path.
G06F 15/16 - Combinations of two or more digital computers each having at least an arithmetic unit, a program unit and a register, e.g. for a simultaneous processing of several programs
H04L 29/08 - Transmission control procedure, e.g. data link level control procedure
H04L 12/28 - Data switching networks characterised by path configuration, e.g. LAN [Local Area Networks] or WAN [Wide Area Networks]
H04L 29/06 - Communication control; Communication processing characterised by a protocol
H04L 12/741 - Header address processing for routing, e.g. table lookup
H04L 29/12 - Arrangements, apparatus, circuits or systems, not covered by a single one of groups characterised by the data terminal
96.
Methods and apparatus for intrusion prevention using global and local feature extraction contexts
In one embodiment, local begin and end tags are detected by a network security device to determine a local context of a network traffic flow, and a local feature vector is obtained for that local context. At least one triggering machine learning model is applied by the network security device to the local feature vector, and the result determines whether or not deeper analysis is warranted. In most cases, very substantial resources are not required because deeper analysis is not indicated. If deeper analysis is indicated, one or more deeper machine learning model may then be applied to global and local feature vectors, and regular expressions may be applied to packet data, which may include the triggering data packet and one or more subsequent data packets. Other embodiments, aspects and features are also disclosed.
An attachment to an e-mail message received at an e-mail gateway is scanned by a scan server and then is converted into an HTML file. The HTML file includes preview data of the attachment (minus any macro scripts), the entire original data of the attachment, scan functionality enabling a user to send the attachment back to a scan server for a second scan, or extract functionality enabling a user to extract the original attachment data for saving or opening in an application. The recipient is able to open or save the attachment directly if he or she believes it comes from a trusted sender. If the attachment seems suspicious, the recipient previews the attachment first before performing a scan, opening the attachment or deleting it. The recipient performs a scan of the attachment by clicking a “scan” button to send the attachment to a backend server for a second scan where an updated virus pattern file may be available to detect any zero-day malware.
A smart home includes Internet of things (IOT) devices that are paired with an IOT gateway. A backend system is in communication with the IOT gateway to receive IOT operating data of the IOT devices. The backend system generates a machine learning model for an IOT device. The machine learning model is consulted with IOT operating data of the IOT device to detect anomalous operating behavior of the IOT device. The machine learning model is updated as more and newer IOT operating data of the IOT device are received by the backend system.
The presently-disclosed solution provides an innovative system and method to protect a computer user from a phishing attack. Computer vision is effectively applied to match identifiable key information in suspect content against a database of identifiable key information of legitimate content. In one embodiment, the presently-disclosed solution converts suspect content to a digital image format and searches a database of logos and/or banners to identify a matching logo/banner image. Once the matching logo/banner image is found, the legitimate domain(s) associated with the matching logo/banner image is (are) determined. In addition, the presently-disclosed solution extracts all the URLs (universal resource links) directly from the textual data of the suspect content and further extracts the suspect domain(s) from those URLs. The suspect domain(s) is (are) then compared against the legitimate domain(s) to detect whether the suspect content is phishing content or not. Other embodiments and features are also disclosed.
H04L 29/06 - Communication control; Communication processing characterised by a protocol
G06K 9/62 - Methods or arrangements for recognition using electronic means
G06F 16/583 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
G06F 16/955 - Retrieval from the web using information identifiers, e.g. uniform resource locators [URL]
A method protects a daemon in an operating system of a host computer. The operating system detects that there is an access of a plist file of a daemon by a process in the computer. If so, then it executes a callback function registered for the plist file. The callback function sends to a kernel extension a notification of the attempted access. The kernel extension returns a value to the operating system indicating that the access should be denied. The operating system denies access to the plist file of the daemon by the process. The extension may also notify an application which prompts the user for instruction. The kernel extension also protects itself by executing its exit function when a command is given to unload the extension, and the exit function determines whether or not the command is invoked by an authorized application, such as by checking a flag.
G06F 21/56 - Computer malware detection or handling, e.g. anti-virus arrangements
G06F 21/52 - Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems during program execution, e.g. stack integrity, buffer overflow or preventing unwanted data erasure
