Actimize Ltd.

Skyhawk Security

Amir Shachar

Chen Burshan

Danny Butvinik

Yoav Avneon

Gabrielle Zaghdoun Azoulay

Shlomi Weizman

Peled Eldan

Einat Neumann Ben Ari

Tsafrir Marom

Liat Elboim

Computerized methods and systems obtain threat data generated from activity data using unsupervised learning. The activity data is collected from enterprises and describes activities performed on the enterprises. The threat data indicates likelihood that sequences of activities performed on the enterprises are indicative of malicious intent. A supervised ML model that processes sequential data is trained by providing a training set of sequential data to the supervised ML model. The training set includes at least some of the obtained threat data, and data derived from activity data collected from at least some of the enterprises. The trained supervised ML receives new data that describes a sequence of activities performed on an enterprise, and processes the received new data to produce a prediction of whether the sequence of activities performed on the enterprise will lead to a malicious action on the enterprise. In some embodiments, multiple supervised ML models are used.

Method and system for early detection of malicious behavior based using self-supervised learning

Computerized methods and systems evaluate threats in a cloud environment having a plurality of assets. For each pair of one or more pairs of the assets, one or more identified paths from a first asset of the pair to a second asset of the pair is obtained. A sequence of assets that includes the first and second assets defines each path of the one or more identified paths. For each path of the one or more identified paths, a likelihood that an attacker that is at the first asset will successfully reach the second asset via the path is determined. In certain embodiments, for each pair of the one or more pairs a risk score for the pair is determined based on the determined likelihoods for the one or more identified paths. The risk score is indicative of risk the attacker will reach the second asset from the first asset.

Methods and systems for threat evaluation based on anticipated pathways of malicious actors

An artificial intelligence system configured to detect anomalies in transaction data sets. The system includes a processor and a computer readable medium operably coupled thereto, the computer readable medium comprising a plurality of instructions stored in association therewith that are accessible to, and executable by, the processor, to perform modeling operations which include receiving a first data set for training a first machine learning model to detect anomalies in the transaction data sets using a machine learning technique, accessing at least one micro-model trained using at least one second data set separate from the first data set, determining risk scores from the first data set using the at least one micro-model, enriching the first data set with the risk scores, and determining the first machine learning model for the enriched first data set using the machine learning technique.

Artificial intelligence system for anomaly detection in transaction data sets

A system is provided for estimating quantile values for fraud assessments. The system includes a processor and a computer readable medium operably coupled thereto, to perform operations which include capturing one or more first data values for a quantile value profile associated with an entity, wherein the quantile value profile includes one of real values or a first plurality of quantile marker values calculated from the real values, accessing the quantile value profile for the entity, determining a first number of the one or more first data values, and based on the first number of the one or more first data values and the one of the real values or the first plurality of quantile marker values in the quantile value profile, performing one of a first merge operation, a second merge operation, or a third merge operation.

Estimating quantile values for reduced memory and/or storage utilization and faster processing time in fraud detection systems

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Ramat Gan, Israel

Amir Shachar