Design of an Ensemble Learning Behavior Anomaly Detection Framework
Abstract:Data assets protection is a crucial issue in the
cybersecurity field. Companies use logical access control tools to
vault their information assets and protect them against external
threats, but they lack solutions to counter insider threats. Nowadays,
insider threats are the most significant concern of security analysts.
They are mainly individuals with legitimate access to companies
information systems, which use their rights with malicious intents.
In several fields, behavior anomaly detection is the method used by
cyber specialists to counter the threats of user malicious activities
effectively. In this paper, we present the step toward the construction
of a user and entity behavior analysis framework by proposing a
behavior anomaly detection model. This model combines machine
learning classification techniques and graph-based methods, relying
on linear algebra and parallel computing techniques. We show the
utility of an ensemble learning approach in this context. We present
some detection methods tests results on an representative access
control dataset. The use of some explored classifiers gives results
up to 99% of accuracy.
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