Demystifying Intrusion Detection: A Path to Enhanced Model Understanding

Abstract:- In the era of digital connectivity, the evolution of cyber threats poses a formidable challenge to our security. Intrusion Detection Systems (IDS) are crucial for identifying and mitigating these threats, but the lack of transparency in some machine learning models used in IDS hampers effective threat response. Explainable Artificial Intelligence (XAI) emerges as a solution, offering insights into complex model deci- sions. This research applies advanced XAI techniques to three core IDS models: Logistic Regression, Random Forest, and Neural Networks. By unraveling the “black box” of these models, we empower security analysts with a deep understanding of alert triggers, facilitating proactive threat management. Our findings have the potential to redefine best practices not only in IDS but also in broader AI-driven security solutions, enhancing our mission to fortify secure digital environments and safeguard the interconnected world from cyber threats.