Autonomous AI-based Cybersecurity Framework for Critical Infrastructure: Real-Time Threat Mitigation

TL;DR

This paper presents a hybrid AI-driven cybersecurity framework designed to improve real-time threat detection, modeling, and automated response in critical infrastructure systems, addressing evolving cyber threats and adversarial AI challenges.

Contribution

It introduces a novel hybrid AI framework that enhances real-time cybersecurity measures specifically tailored for critical infrastructure resilience.

Findings

Improved real-time threat detection accuracy

Effective automated remediation strategies

Insights into adversarial AI challenges

Abstract

Critical infrastructure systems, including energy grids, healthcare facilities, transportation networks, and water distribution systems, are pivotal to societal stability and economic resilience. However, the increasing interconnectivity of these systems exposes them to various cyber threats, including ransomware, Denial-of-Service (DoS) attacks, and Advanced Persistent Threats (APTs). This paper examines cybersecurity vulnerabilities in critical infrastructure, highlighting the threat landscape, attack vectors, and the role of Artificial Intelligence (AI) in mitigating these risks. We propose a hybrid AI-driven cybersecurity framework to enhance real-time vulnerability detection, threat modelling, and automated remediation. This study also addresses the complexities of adversarial AI, regulatory compliance, and integration. Our findings provide actionable insights to strengthen the security and resilience of critical infrastructure systems against emerging cyber threats.