Enhanced Intrusion Detection System using Deep Learning with Explainable AI and GenAI

This project aims to improve the cybersecurity defense by taking advantage of deep learning, XAI, and GenAI for improving the current system. For instance, its traditional IDS models have a poor capability to monitor sophisticated cyber threats and many IDS models are based on a high degree of a black-box model that is difficult for security analysts to comprehend. We use deep learning in our approach to achieve higher detection accuracy, and rely on XAI (eXplainable Artificial Intelligence) techniques such as SHAP (SHapley Additive exPlanations), LIME (Local Interpre table Model agnostic Explanations), to add interpretability to a model's decisions and allow for building trust towards automated systems. Also, humans lack cybersecurity data to label and the scarcity of data forces us to create synthetic data so the deep learning model can process different attack scenarios. This coupled with these two aims aims to ultimately decrease false positives and improve real time detection capabilities in a more reliable and powerful intrusion detection system against dynamic, evolving cyber threat landscapes.