Towards Explainable Machine Learning in Intrusion Detection Systems

Author (ESR): 
Ly Vu Duc (Universita Degli Studi Di Trento)
Chau D.M. Pham
Duc-Ly Vu
Fabio Massacci
Tran Khanh Dang
Sandro Etalle
Davide Fauri


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.

ESSoS: Engineering Secure Software and Systems
Thursday, August 1, 2019