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Artificial Intelligence (AI) has become an integral part of modern-daysecurity solutions for its ability to learn very complex functions and handling"Big Data". However, the lack of explainability and interpretability ofsuccessful AI models is a key stumbling block when trust in a model'sprediction is critical. This leads to human intervention, which in turn resultsin a delayed response or decision. While there have been major advancements inthe speed and performance of AI-based intrusion detection systems, the responseis still at human speed when it comes to explaining and interpreting a specificprediction or decision. In this work, we infuse popular domain knowledge (i.e.,CIA principles) in our model for better explainability and validate theapproach on a network intrusion detection test case. Our experimental resultssuggest that the infusion of domain knowledge provides better explainability aswell as a faster decision or response. In addition, the infused domainknowledge generalizes the model to work well with unknown attacks, as well asopens the path to adapt to a large stream of network traffic from numerous IoTdevices.

Domain Knowledge Aided Explainable Artificial Intelligence for Intrusion Detection and Response