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Intrusion Detection System for Open Network Scenarios: A Known/Unknown Attack Detection Method Based on Inter-Class Relationships
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Summary
This paper proposes a known/unknown attack detection method for intrusion detection systems that uses inter-class relationships, with a virtual class inserted near known attack categories and soft-label prototypes compared to predictions via Kullback-Leibler divergence. A Gaussian Mixture Model models the divergence distribution to identify unknown attacks. On NSL-KDD, the method improves the harmonic mean of known and unknown class detection accuracy by over 7% relative to state-of-the-art methods, and it raises unknown attack detection accuracy by about 1.12% on CICIDS2017 and 5.24% on NSL-KDD.