L-XAIDS: A LIME-based eXplainable AI framework for Intrusion Detection Systems

Explainability is critical for AI in high-stakes domains: The paper emphasizes that transparency in AI decision-making is especially important in cybersecurity, healthcare, fintech, and autonomous vehicles, where incorrect decisions can have serious consequences.

Black-box AI systems in cybersecurity lack transparency: Machine Learning-based Intrusion Detection Systems (IDS) currently operate as "black boxes," making it difficult for security professionals to understand why the system flagged certain network traffic as malicious, which can hinder adoption and trust.

The L-XAIDS framework provides two levels of explanation: The proposed solution offers both local explanations (why a specific network event was classified as an attack) and global explanations (which features are most important for detecting attacks overall), giving users insight at different levels of detail.

Explainability may accelerate real-world adoption of AI security tools: The paper suggests that bringing transparency to machine learning-driven intrusion detection systems "might be highly significant for wide scale adoption," indicating that organizations may be more willing to deploy AI security tools if they can understand and verify the system's reasoning.

Multiple explanation techniques can work together: By combining LIME (a model-agnostic explanation method) with simpler approaches like decision trees and plain-language explanations (ELI5), the framework makes AI decisions more interpretable to different types of users.