H04L 41/0631 - Management of faults, events, alarms or notifications using root cause analysisManagement of faults, events, alarms or notifications using analysis of correlation between notifications, alarms or events based on decision criteria, e.g. hierarchy, tree or time analysis
G05B 23/00 - Testing or monitoring of control systems or parts thereof
G06N 20/10 - Machine learning using kernel methods, e.g. support vector machines [SVM]
2.
CONTROL SYSTEM ANOMALY DETECTION USING NEURAL NETWORK CONSENSUS
H04L 41/0631 - Management of faults, events, alarms or notifications using root cause analysisManagement of faults, events, alarms or notifications using analysis of correlation between notifications, alarms or events based on decision criteria, e.g. hierarchy, tree or time analysis
G06N 20/10 - Machine learning using kernel methods, e.g. support vector machines [SVM]
3.
Control system anomaly detection using neural network consensus
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 41/16 - Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks using machine learning or artificial intelligence
42 - Scientific, technological and industrial services, research and design
Goods & Services
Cybersecurity services in the nature of finding vulnerabilities to help restrict unauthorized access to computer systems; Online non-downloadable cybersecurity software
Gyroscope data can be used to generate upsampled signal. Multiple mobile devices are spaced apart from each other in a spatial arrangement. Each mobile device includes a gyroscope sensor to detect mechanical vibrations caused by signals originating within a vicinity of a mobile device that includes the gyroscope sensor. Each mobile device includes one or more respective processors to receive representations of the mechanical vibrations sensed by the gyroscope sensor at a sampling frequency, and transmit the representations received at the sampling frequency as a respective vibration signal associated with sampling times. The signal processor is coupled to the multiple mobile devices. The signal processor generates a processed upsampled signal by interleaving the vibration signal received from each mobile device and processing the interleaved signal using one or more machine learning filters, and transmitting the processed upsampled signal.
G01C 19/5698 - Turn-sensitive devices using vibrating masses, e.g. vibratory angular rate sensors based on Coriolis forces using acoustic waves, e.g. surface acoustic wave gyros
G01C 19/5783 - Mountings or housings not specific to any of the devices covered by groups
G01C 19/5776 - Signal processing not specific to any of the devices covered by groups
G01C 19/50 - Erection devices for restoring rotor axis to a desired position operating by mechanical means
In some implementations, a method includes retrieving data from multiple sensors in a computing device, and the multiple sensors comprise different types of sensors. The sensor data is analyzed based on a predictive model, and the predictive model is trained to detect malware. Initiation of malware is determined based on the analysis. In response to the determination, the malware is terminated.
G06F 21/56 - Computer malware detection or handling, e.g. anti-virus arrangements
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 21/57 - Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
In some implementations, a method includes retrieving data from multiple sensors in a computing device, and the multiple sensors comprise different types of sensors. The sensor data is analyzed based on a predictive model, and the predictive model is trained to detect malware. Initiation of malware is determined based on the analysis. In response to the determination, the malware is terminated.
G06F 21/56 - Computer malware detection or handling, e.g. anti-virus arrangements
G06F 21/50 - Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems
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 21/57 - Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
Methods, apparatuses, and computer program products for squaring an operand include identifying a fixed-point value with a fixed word size and a substring size for substrings of the fixed-point value, wherein the fixed-point value comprises a binary bit string. A square of the fixed-point value can be determined using the fixed point value, the substring size, and least significant bits of the fixed-point value equal to the substring size.
G06F 7/544 - Methods or arrangements for performing computations using exclusively denominational number representation, e.g. using binary, ternary, decimal representation using non-contact-making devices, e.g. tube, solid state deviceMethods or arrangements for performing computations using exclusively denominational number representation, e.g. using binary, ternary, decimal representation using unspecified devices for evaluating functions by calculation
A multi-valued logic (MVL) circuit includes a MVL clock generator that generates a MVL clock signal having three or more ith MVL levels, a single MVL clock signal distribution network connected to the MVL clock generator, and three or more ith MVL selection circuits connected to the single MVL clock signal distribution network where i=0 to N and N>=3. Each ith MVL selection circuit corresponds to a specified ith MVL level. The ith MVL selection circuit outputs an ith binary clock signal having: (a) a first logic level whenever the MVL clock signal is equal to the ith MVL level and the ith data input receives the first logic level, (b) a second logic level whenever the MVL clock signal is equal to the ith MVL level and the ith data input receives the second logic level, and (c) a previous logic level of the ith binary clock signal whenever the MVL clock signal is not equal to the ith MVL level.
The present invention provides a device and method for classifying a user using pattern recognition of an input device. A series of the keystroke objects are received via the user input interface. A typing signature is determined for the series of keystroke objects using the processor by analyzing the key attributes of the series of keystroke objects using a pattern recognition algorithm. The typing signature is compared to one or more user typing signatures stored in the memory using the processor. The user is classified based on whether or not the typing signature is statistically similar to one of the stored typing signatures.
G06F 3/023 - Arrangements for converting discrete items of information into a coded form, e.g. arrangements for interpreting keyboard generated codes as alphanumeric codes, operand codes or instruction codes
