An Explainable and Optimized Network Intrusion Detection Model using Deep Learning

In the current age, internet and its usage have become a core part of human existence and with it we have developed technologies that seamlessly integrate with various phases of our day to day activities. The main challenge with most modern-day infrastructure is that the requirements pertaining to security are often an afterthought. Despite growing awareness, current solutions are still unable to completely protect computer networks and internet applications from the ever -evolving threat landscape. In the recent years, deep learning algorithms have proved to be very efficient in detecting network intrusions. However, it is exhausting, time-consuming, and computationally expensive to manually adjust the hyper parameters of deep learning models. Also, it is important to develop models that not only make accurate predictions but also help in understanding how the model is making those predictions. Thus, model explainability helps in crease user's trust. The current research gap in the domain of Network Intrusion Detection is the absence of a holistic framework that incorporates both optimization and explainable methods. In this research article, a hybrid approach to hyper parameter optimization using hyperband is proposed. An overall accuracy of 98.58% is achieved by considering all the attack types of the CSE CIC 2018 dataset. The proposed hybrid framework enhances the performance of Network Intrusion Detection by choosing an optimized set of parameters and leverages explainable AI (XAI) methods such as Local Interpretable Model agnostic Explanations (LIME) and SHapely Additive exPlanations (SHAP) to understand model predictions.