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