  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 Self
Organizing Maps (SOMs) based X-IDS system 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.
