| Autor: |
Jesse Ables, Thomas Kirby, William Anderson, Sudip Mittal, Shahram Rahimi, Ioana Banicescu, Maria Seale |
| Rok vydání: |
2022 |
| Předmět: |
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| DOI: |
10.48550/arxiv.2207.07465 |
| Popis: |
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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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