Intrusion detection and response system generator: using transferred belief model

This article proposes an intrusion detection and response system using the Smets' transferable belief model (TBM). The system is trained using data on attack classes expressed using Shafer's belief functions, and is hence capable of learning new attacks. Network sensors feed the system belief model before a pignistic model is developed. A risk-driven response subsystem is then generated. The generated system is capable of classifying new intrusion patterns and plan responses to enforce an acceptable risk position as indicated in the corporate security policy.

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