AI-Driven Autonomous Cyber Defense for Critical Infrastructure

Abstract

As critical infrastructure such as energy grids, healthcare systems, water treatment plants and communication networks becomes increasingly exposed to sophisticated, multi-vector cyber attacks that exceed traditional rule-based security solutions, they must become more secure. Such attacks are becoming more frequent and complex and require new, adaptive defense solutions to effectively combat them with intelligent, real-time autonomous decision-making. The paper introduces a thorough study into design, implementation and evaluation of an AI-Driven Autonomous Cyber Defense (AI-ACD) system tailored for critical infrastructure protection. The proposed architecture is a layered self-learning defence stack of three complementary categories of Artificial Intelligence: Supervised machine learning (Random Forest or Support Vector Machines), Deep learning (Convolutional Neural Networks or Long Short-Term Memory networks), and Reinforcement learning (Deep Q-Networks or Proximal Policy Optimization). On two benchmark datasets, namely CICIDS2017 and UNSW-NB15, the proposed Hybrid AI model has been experimental evaluated and showed the detection accuracy of 97.6%, precision of 97.1%, recall of 97.8% and F1-score of 97.4%, which is much better than the individual Baseline Models. Additionally, the autonomous response mechanism cuts the mean time to mitigate threat down to less than three seconds for each of the attack types tested such as Distributed Denial-of-Service (DDoS), zero-day exploits, and Advanced Persistent Threat (APT) from a human analyst baseline of 42-72 seconds. The paper also discusses several key implementation issues, such as robustness to adversarial attacks, adherence to data privacy regulations, model explainability, and the ethical use of independent AI for safety-critical applications. The results validate the fact that the implementation of autonomous systems with AI in the cybersecurity domain of CNF constitutes a prior revolution in the posture of cybersecurity systems.