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The current state of the art systems in Artificial Intelligence (AI) enabledintrusion detection use a variety of black box methods. These black box methodsare generally trained using Error Based Learning (EBL) techniques with a focuson creating accurate models. These models have high performative costs and arenot easily explainable. A white box Competitive Learning (CL) based eXplainableIntrusion Detection System (X-IDS) offers a potential solution to theseproblem. CL models utilize an entirely different learning paradigm than EBLapproaches. This different learning process makes the CL family of algorithmsinnately explainable and less resource intensive. In this paper, we create anX-IDS architecture that is based on DARPA's recommendation for explainablesystems. In our architecture we leverage CL algorithms like, Self OrganizingMaps (SOM), Growing Self Organizing Maps (GSOM), and Growing Hierarchical SelfOrganizing Map (GHSOM). The resulting models can be data-mined to createstatistical and visual explanations. Our architecture is tested using NSL-KDDand CIC-IDS-2017 benchmark datasets, and produces accuracies that are 1% - 3%less than EBL models. However, CL models are much more explainable than EBLmodels. Additionally, we use a pruning process that is able to significantlyreduce the size of these CL based models. By pruning our models, we are able toincrease prediction speeds. Lastly, we analyze the statistical and visualexplanations generated by our architecture, and we give a strategy that userscould use to help navigate the set of explanations. These explanations willhelp users build trust with an Intrusion Detection System (IDS), and allowusers to discover ways to increase the IDS's potency.

Explainable Intrusion Detection Systems Using Competitive Learning Techniques