International Business Machines Corporation

Rosa Miroshnikov

Oded Sofer

Allon Adir

Ehud Aharoni

Lev Greenberg

Boris Rozenberg

Oded Margalit

Ofer Haim Biller

David Rozenblat

Embodiments of the present invention may provide the capability to identify security breaches in computer systems from clustering properties of clusters generated based on monitored behavior of users of the computer systems by using techniques that provide improved performance and reduced resource requirements. For example, behavior of users or resources may be monitored and analyzed to generate clusters and train clustering models. Labeling information relating to some user or resource may be received. When users or resources are clustered and when a cluster contains some labeled users/resources then an anomaly score can be determined for a user/resource belonging to the cluster. A user or resource may be detected to be an outlier of at least one cluster to which the user or resource has been assigned, and an alert indicating detection of the outlier may be generated.

Identifying security breaches from clustering properties

In some examples, a system for detecting unauthorized data access can include a processor to detect a suspicious operation to be executed by the system and review a plurality of highlights corresponding to the suspicious operation. The processor can also determine that a predefined cyber security image corresponding to the highlights and the suspicious operation does not exist and generate the predefined cyber security image based on a plurality of sub-cyber security images. Furthermore, the processor can store the predefined cyber security image in a cyber security image repository and prevent the suspicious operation from being executed.

Unauthorized data access detection based on cyber security images

A system for detecting cyber security events can include a processor to generate a first set of a plurality of time series and aggregate statistics based on a plurality of properties corresponding to user actions for each user in a set of users. The processor can also separate the set of users into a plurality of clusters based on the first set of the plurality of time series or aggregate statistics for each user and assign an identifier to each of the plurality of clusters. Additionally, the processor can generate a second set of a plurality of time series based on properties of the plurality of clusters, wherein the properties of a cluster correspond to a membership, a diameter, and a centroid and detect an anomaly based on a new value stored in the second set of the time series. Furthermore, the processor can execute a prevention instruction.

Cyber security event detection

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Vaughan, Canada

Rosa Miroshnikov