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User and entity behavior analytics (UEBA) is a type of machine learning model that can help to foil cyberattackers by discovering security anomalies. UEBA uses advanced analysis, aggregates data from logs and reports, and looks at packet, flow, file and other types of information, as well as certain kinds of threat data to figure out whether certain kinds of activity and behavior are likely to constitute a cyberattack.
As some experts point out, user and entity behavior analytics applies to different kinds of software and hardware architectures. It can monitor user accounts, work with endpoint devices, or be embedded in applications and networks. These models help to detect certain kinds of anomalies that might indicate malicious behavior. This type of analytics is also a learning system, utilizing machine learning capabilities, to build better security over repetitive use.
Tech experts call user and entity behavior analytics a better model for attack detection and maintain that it is going to enable more accurate detection of bad actors threatening networks.