Explainable and Adversarially Robust AI for Cyber Defense in Critical Infrastructure Systems
Keywords:
Critical Infrastructure Security, Explainable Artificial Intelligence, Adversarial Machine Learning, Cyber Defense, Machine Learning, Deep Learning, Robust AI ModelsAbstract
Critical infrastructure systems (CIS) including water delivery facilities and electrical connections, transport, healthcare, and communication networks are being increasingly targeted by highly advanced forms of cyber-attack because they are digital and interconnected. Ransomware, phishing, DDoS, and APTs are among the advanced attacks that traditional cybersecurity mechanisms are inadequate. Artificial Intelligence (AI) has become a game-changer for cyber defense, offering cutting-edge capabilities in intrusion detection, anomaly detection, and predictive analysis. In the realm of cyber defense, AI is a revolutionary tool that brings advanced features such as intrusion detection, anomaly detection, and predictive analytics. But in most AI models, the black-box nature is not conducive to transparency and trust, especially in critical decision-making scenarios. Explainable Artificial Intelligence (XAI) offers solutions to this, as it makes the insights obtained by AI models interpretable through tools like SHAP, LIME, and rule-based models. Meanwhile, adversarial machine learning reveals the weaknesses in AI systems that could impact performance or be overlooked when attackers input data. In this paper, the synergy of explainable AI and adversarially robust AI in the context of securing critical infrastructure systems. It covers important techniques, defense mechanisms and problems, and shows the compromises in robustness and interpretation in high-risk Cybersecurity environments
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