Creating an Explainable Intrusion Detection System Using Self Organizing Maps

Jesse Ables, Thomas Kirby, William Anderson, Sudip Mittal, Shahram Rahimi +2 more
7/15/2022
cs.CRcs.AIcs.LG

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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 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.

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Cite this paper

@article{ables2022creating,
  title  = {Creating an Explainable Intrusion Detection System Using Self Organizing Maps},
  author = {Jesse Ables and Thomas Kirby and William Anderson and Sudip Mittal and Shahram Rahimi and Ioana Banicescu and Maria Seale},
  year   = {2022},
  eprint = {2207.07465},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2207.07465v1}
}

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