Creating an Explainable Intrusion Detection System Using Self Organizing Maps
Jesse Ables, Thomas Kirby, William Anderson, Sudip Mittal, Shahram Rahimi, Ioana Banicescu, Maria Seale
Abstract
Modern Artificial Intelligence (AI) enabled Intrusion Detection Systems (IDS) are complex black boxes. This means that a security analyst will have little to no explanation or clarification on why an IDS model made a particular prediction. A potential solution to this problem is to research and develop Explainable Intrusion Detection Systems (X-IDS) based on current capabilities in Explainable Artificial Intelligence (XAI). In this paper, we create a novel X-IDS architecture featuring a Self Organizing Map (SOM) that is capable of producing explanatory visualizations. We leverage SOM's explainability to create both global and local explanations. An analyst can use global explanations to get a general idea of how a particular IDS model computes predictions. Local explanations are generated for individual datapoints to explain why a certain prediction value was computed. Furthermore, our SOM based X-IDS was evaluated on both explanation generation and traditional accuracy tests using the NSL-KDD and the CIC-IDS-2017 datasets. This focus on explainability along with building an accurate IDS sets us apart from other studies.