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.
|Publication status||Published - 24 Dec 2016|
|Event||5th IEEE-EDS International Conference on Computing, Communication and Sensor Network - Kolkata, India|
Duration: 24 Dec 2016 → 25 Dec 2016
|Conference||5th IEEE-EDS International Conference on Computing, Communication and Sensor Network|
|Abbreviated title||CCSN 2016|
|Period||24/12/16 → 25/12/16|