The rapid growth of Internet of Things(IoT) has reached all the facets of life including critical infrastructures. It has become the foundation for most of the critical infrastructures. The increased connectivity and the heterogeneity in IoT have widened the attack surface of critical infrastructures for attackers to exploit. Certain cyberattacks in critical infrastructures can lead to catastrophe and hence the attack has to be identified as early as possible to stop or reduce its impact by activating suitable responses. Therefore, the critical infrastructures require an intelligent security mechanism which can intelligently interpret the attacks from the IoT traffic and efficiently handle the attack scenario by activating appropriate response at faster rate. In this work, an autonomic security system with intelligent self-protect mechanism has been proposed for critical infrastructures. The autonomic security system can autonomously detect known attacks using Extreme Learning Machine, predict the unknown attacks using Gaussian process regression, and select suitable response for handling the attack using fuzzy logic. This intelligence of self-protect mechanism is incorporated in the distributed fog nodes to handle the attack scenario at faster rate and protect the critical infrastructures with minimal human intervention. The experimental analysis of the proposed autonomic security system proves to be efficient in detecting and defending the cyber-attacks with high accuracy and success rate. The results on network load and response time indicates the effectiveness of fog computing in proposed system.

critical infrastructure, Internet of Things, autonomic system, fog computing, Gaussian process regression, extreme learning machine