Anomaly detection using cognitive computing

Abstract

An anomaly detection system configured to generate a plurality of tensors based on spatial attributes of a set of cybersecurity data and temporal attributes of the set of cybersecurity data. The set of cybersecurity data comprising numeric data and textual data collected from a plurality of computational sources. The anomaly detection system can provide the plurality of tensors to a Hierarchical Temporal Memory (HTM) network. The HTM network can be configured to generate respective HTM outputs for respective regions of the HTM network. The anomaly detection system can determine that at least one HTM output indicates an anomaly, convert the at least one HTM output to a notification, and provide the notification to a user interface.

Description

technical field [0001] The present disclosure relates to computer security, and more particularly, to anomaly detection systems. Background technique [0002] The anomaly detection system may be used as a component of a cybersecurity system configured to maintain the confidentiality, integrity, and / or availability of data stored in, executed on, and and / or transfer between data processing systems. For example, an anomaly detection system may identify unsafe, unexpected, unusual, suspicious, malicious, and / or unauthorized activity occurring within, between, or throughout portions of a cybersecurity environment. [0003] Identifying abnormal conditions within an information system environment is one of the most effective ways to detect cyberattacks, security incidents, and threats. Detecting this condition is the first step any organization needs to take in order to be able to respond in a timely manner to protect critical information assets in the IT environment from being ...

AI Technical Summary

Problems solved by technology

Detecting anomalies and properly understanding and correlating to specific security or threat classifications and responses is a challenging process

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