Thetaray Ltd.

Thetaray Lid.

David Segev

Gil Shabat

Amir Averbuch

Erez Shabat

Amit Bermanis

Methods for anomaly detection using dictionary based projection (DBP), and system for implementing such methods. In an embodiment, a method comprises receiving input data including a plurality n of multidimensional data points (MDDPs) with dimension m, applying DBP iteratively to the input data to construct a dictionary D, receiving a newly arrived MDDP (NAMDDP), calculating a score S associated with the NAMDDP as a distance of the NAMDDP from dictionary D, and classifying the NAMDDP as normal or as an anomaly based on score S, wherein classification of the NAMDDP as an anomaly is indicative of detection of an unknown undesirable event.

Anomaly detection using dictionary based projection

Detection systems, methods and computer program products comprising a non-transitory tangible storage medium readable by a processing circuit and storing instructions for execution by the processing circuit for performing a method for anomaly detection, a detected anomaly being indicative of an undesirable event. A detection system comprises a computer and an anomaly detection engine executable by the computer, the anomaly detection engine configured to perform a method comprising receiving data comprising a plurality m of multidimensional data points (MDDPs), each data point having n features, constructing a dictionary D based on the received data, embedding dictionary D into a lower dimension embedded space and classifying, based in the lower dimension embedded space, a MDDP as an anomaly or as normal.

System and method for anomaly detection in dynamically evolving data using random neural network decomposition

A computer program product for performing anomaly detection, a detected anomaly being indicative of an undesirable event, the computer program product comprising: a non-transitory tangible storage medium readable by a processing circuit and storing instructions for execution by the processing circuit for performing a method comprising: receiving data comprising a plurality m of multidimensional datapoints (MDDPs), each data point having n features; constructing a dictionary D based on the received data; embedding dictionary D into a lower dimension embedded space; and classifying, based in the lower dimension embedded space, an MDDP as an anomaly or as normal.

System and method for anomaly detection in dynamically evolving data using hybrid decomposition

A system for detecting an unknown undesirable event comprises an input device configured to receive a dataset comprising a plurality n of multidimensional datapoints (MDDPs), a processor configured to embed the MDDPs in an lower dimension embedded space to obtain embedded MDDPs, and a detection engine configured to calculate distributions of distances D, i=1, . . . , n of each embedded MDDP from a plurality of nearest-neighbors (nn) to compute a threshold Dand to classify a particular MDDP of the dataset or a newly arrived MDDP (NAMDDP) as an abnormal MDDP based on comparison with threshold D, wherein the classification is automatic and unsupervised without relying on a signature, rules or domain expertise and wherein the particular MDDP classified as abnormal is indicative of the unknown undesirable event.

System and method for detecting an undesirable event

Method and system for detecting an unknown undesirable event, such as (but not limited to) a cyber-threat, a cyber-intrusion, a financial fraud event or a monitored process malfunction of breakdown. An exemplary method embodiment comprises obtaining a dataset comprising a plurality n of multidimensional data points with a dimension m&#x2265;2 wherein each data point is a vector of m features, processing the MDPs using measure-based diffusion maps to embed the MDPs into a lower dimension embedded space, and detecting in the embedded space an abnormal MDP without relying on a signature of a threat, the abnormal MDP being indicative of the unknown undesirable event.

Measure-based anomaly detection

A computer program product for performing anomaly detection, a detected anomaly being indicative of an undesirable event, the computer program product comprising a non-transitory tangible storage medium readable by a processing circuit and storing instructions for execution by the processing circuit for performing a method comprising receiving data comprising a plurality m of multidimensional datapoints (MDDPs), each data point having n features, constructing a dictionary D based on the received data, embedding dictionary D into a reduced dimension embedded space and classifying, based in the reduced dimension embedded space, an MDDP as an anomaly or as normal.

A computer program product for performing anomaly detection, a detected anomaly being indicative of an undesirable event, the computer program product comprising a non-transitory tangible storage medium readable by a processing circuit and storing instructions for execution by the processing circuit for performing a method comprising receiving data comprising a plurality m of multidimensional datapoints (MDDPs), each data point having n features, constructing a dictionary D based on the received data, embedding dictionary D into a lower dimension embedded space and classifying, based in the lower dimension embedded space, an MDDP as an anomaly or as normal.

Method and system for detecting an unknown undesirable event, such as (but not limited to) a cyber-threat, a cyber-intrusion, a financial fraud event or a monitored process malfunction of breakdown. An exemplary method embodiments comprises obtaining a dataset comprising a plurality n of multidimensional data points with a dimension m&#x2265;2 wherein each data point is a vector of m features, processing the MDPs using measure-based diffusion maps to embed the MDPs into a lower dimension embedded space, and detecting in the embedded space an abnormal MDP without relying on a signature of a threat, the abnormal MDP being indicative of the unknown undesirable event.

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Lapid, Israel

David Segev