Optimizing Threat Mitigation in Critical Infrastructure through AI-Driven Cybersecurity Solutions
Shantanu Sudhir Gujar
Abstract
In this paper, the author proposes a novel AI-based cybersecurity model meant for improving threat identification and response in critical infrastructure contexts. Using highly developed methods of artificial neural networks, the system adapts to network flows, logs and system outages and identifies and counteracts increasingly complex cyber threats. The product was developed and validated through exercise scenarios in order to evaluate the impact on sectors of critical infrastructure such as energy, transport, healthcare and others. The outcomes show that the system has received substantial enhancements in threat detection of multiple classes, with classification level of 94% and the false positive levels of 4%. The large-scale AI system was shown to be able to attain better scalability than the model trained on the local set without decreased performance during the high network utilization. Moreover, time responses for threat counteraction reduced dramatically as the system developed through iterations, demonstrating its real-time learning ability. It also describes difficulties which appear when applying the solution, for example, when it comes to data variety and integration of AI models with existing systems. Nonetheless, the solution that is proposed herein has the potential for achieving a scalable and adaptive security in key sectors.