Towards Explainable Machine Learning in Intrusion Detection Systems

Poster

The lacking of semantics or reasonable explanations for machine learning predictions is one of the main reasons for its adoption barrier in the field of intrusion detection. In fact, without explanations, one machine learning approach fails to gain users’ trust in its predictions, especially after having wasted their effort for examining false-positives. Therefore, we are motivated to study a novel approach of applying machine learning such that it not only efficiently detects intrusions but also provides explanations for those decisions.