Other

Thetaray Ltd.

Ramot at Tel-aviv University Ltd.

Infowrap Systems Ltd

Amir Averbuch

Menachem Nathan

Michael Golub

Valery Zheludev

Ronald R Coifman

David Segev

Gil Shabat

Gil David

Roman Malinsky

Erez Shabat

Asaf Kagan

Ofer Miller

Yaniv Shmueli

Yossi Zlotnick

Alon Schclar

Aviram Shmueli

Shmuel Rosset

Yossi Abarbanel

Anthony A Vaddiliou

Francois G Meyer

JanOlov Str&ouml;mberg

Devid Segev

Amit Bermanis

Moshe Guttmman

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

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

Detection of abnormalities in HDBD is performed by processing it to obtain a dictionary from a training data. This is done by computing a low rank randomized LU decomposition which enables constant online updating of the training data and thus gets constant updating of the normal profile in the background.

System and method for anomaly detection in dynamically evolving data using low rank matrix decomposition

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

Detection of abnormalities in multi-dimensional data is performed by processing the multi-dimensional data to obtain a reduced dimension embedding matrix, using the reduced dimension embedding matrix to form a lower dimension (of at least 2D) embedded space, applying an out-of-sample extension procedure in the embedded space to compute coordinates of a newly arrived data point and using the computed coordinates of the newly arrived data point and Euclidean distances to determine whether the newly arrived data point is normal or abnormal.

Anomaly detection in dynamically evolving data and systems

Apparatus and method for obtaining a plurality of spectral images of a source object in a snapshot using comprising two-dimensional compressed sensing data cube reconstruction (2D CS-SCR) applied to a dispersed-diffused snapshot image. In some embodiments, the snapshot image is obtained through a RIP diffuser. In some embodiments, a randomizer is used to further randomized the dispersed-diffused snapshot image. The 2D CS-SCR includes applying a 2D framelet transform separately to arrays representing different wavebands of spectral cube data derived from the snapshot image. The application of the 2D framelet transform separately to the arrays representing the different wavebands includes application of direct and inverse 2D framelet transforms to the arrays. In some embodiments, the direct and inverse framelet transforms are included in a split Bregman iteration.

Apparatus and method for snapshot spectral imaging

Measure based anomaly detection

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.

Snapshot spectral imagers comprise an imaging lens, a dispersed image sensor and a restricted isometry property (RIP) diffuser inserted in the optical path between the source image and the image sensor. The imagers are used to obtain a plurality of spectral images of the source object in different spectral bands in a single shot. In some embodiments, the RIP diffuser is one dimensional. An optional disperser may be added in the optical path, to provide further dispersion at the image sensor. In some embodiments, all imager components except the RIP diffuser may be part of a digital camera, with the RIP diffuser added externally. In some embodiments, the RIP diffuser may be included internally in a digital camera.

Snapshot spectral imaging based on digital cameras

Monochromatic cameras and methods for using such cameras to obtain a still or video color image of an object or scene. The image sensor of such cameras is clear, without a color filter array. A diffused-dispersed and optionally randomized image of the object or scene obtained at the image sensor is processed directly into a number R&#x3c;K of spectral images in R wavebands. K&#x2212;R spectral images are interpolated from the R spectral images. A color image is then reconstructed using the directly processed R spectral images and the K&#x2212;R interpolated spectral images. The interpolated images may exemplarily be obtained using a spline subdivision algorithm.

Growing community of inventors

Tel Aviv, Israel

Amir Averbuch