Das, S

Issac, Biju

Kalita, Hemanta Kumar

Sharma, Rupam Kumar

Machine learning has proven to enhance the detection rate by any Intrusion Detection Engine. However, if learning is tampered than there is a likeliness that the entire detection engine might fail. This paper explores the adversary learning by SOM (Self Organizing Map) trained on NSL-KDD data set. Experimental results demonstrate the deviation of the learning behaviour after being trained with crafted poison packets to mislead the learning

Learning is never secure: Poison learning by Intrusion Detection System based on Self-Organizing Map

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Teeside University's Research Repository

Teeside University's Research Repository