X-CBA: Explainability Aided CatBoosted Anomal-E for Intrusion Detection System

Explainability is essential for cybersecurity trust: The paper identifies a significant "transparency gap" in current machine learning-based intrusion detection systems, noting that non-transparent decision-making creates a "trust deficit" that undermines confidence and accountability in these critical security tools.

High accuracy alone is insufficient: While X-CBA achieves 99.47% accuracy in threat detection, the researchers emphasize that this performance means little without explainability—the system provides "clear, actionable explanations" alongside its predictions to enable human oversight and validation of security decisions.

Explainability enables broader AI adoption in cybersecurity: The paper argues that offering both local and global explainability solutions is necessary to "facilitate the broader integration of ML/DL technologies in cybersecurity defenses," suggesting that transparency is a prerequisite for responsible deployment of AI in critical infrastructure protection.

Better data representation improves both performance and interpretability: Unlike previous approaches that rely only on labeled data and node features, X-CBA uses a broader range of network traffic information including edge attributes and packet-level data, demonstrating that more comprehensive data representation can enhance both detection capabilities and the ability to explain why threats were identified.