Explainable Intrusion Detection Systems (X-IDS): A Survey of Current Methods, Challenges, and Opportunities

AI-powered intrusion detection systems achieve high accuracy but lack transparency: While machine learning-based Intrusion Detection Systems (IDS) are effective at processing large volumes of network data and identifying threats, many use "black box" deep learning models that provide no explanation for why they flagged something as malicious, making it difficult for cybersecurity analysts to trust or act on their alerts.

Unexplainable AI creates a critical gap in cybersecurity decision-making: Cybersecurity Operation Center (CSoC) analysts cannot improve their threat response decisions when AI systems don't justify their predictions, potentially leading to missed security insights or inappropriate responses to detected threats.

There's a fundamental tradeoff between accuracy and explainability: The survey identifies that "white box" approaches (which are more transparent and interpretable) and "black box" approaches (which are more accurate) present competing advantages, meaning organizations must balance how much they prioritize understanding why a threat was detected versus how accurately threats are detected overall.

Explainable AI for cybersecurity is an emerging but underdeveloped field: The survey reviews current methods for making IDS explainable (X-IDS) but indicates this remains an area with significant challenges and opportunities, suggesting the field is still developing practical solutions for this critical security need.