Development of AI-Driven Machine Learning Systems for Real-Time Detection and Automatic Mitigation of Advanced Cyber Threats Across Critical Infrastructure

Abstract

A growing threat to critical infrastructure systems including energy grid, water treatment plants, transportation and health care systems has emerged as a result of advanced cyber-attack methods (advanced persistent threats (APTs), ransom ware, and coordinated denial-of-service attacks). The conventional signature-based and passive intrusion detection mechanisms are no longer adequate. The proposed article suggests the creation of AI-driven machine learning (ML) systems to be used to detect anomalous network and system behaviors in real-time and automatically mitigate detected threats in a critical infrastructure setup. The suggested system incorporates supervised and unsupervised ML algorithms, deep-learning systems, and reinforcement-learning agents to observe real-time information streams of industrial control systems (ICS), supervisory control and data acquisition (SCADA) networks, and IoT-enabled equipment. After a threat has been detected, the platform initiates automated mitigation measures, e.g. network segmentation, firewall rulebook, process isolation, or threat-intelligence sharing, to reduce the effect of the threat and downtime. The article introduces an architecture for the framework, scalability, and low latency considerations of the implementation, and a test based on simulation data of representative infrastructure environments. Findings reveal that the AI-based system is capable of identifying new threats much quicker than old-fashioned and minimizes the incidence of false-positive outcomes as well as the possibility of taking mitigation measures within tolerable time frames to maintain an operational resilience. Deep-learning decisions might be interpreted with limitations, and integrating deep learning with legacy infrastructure is a problem. The paper provides an example of the resilience of critical infrastructure through the application of AI-ML systems that prepare the infrastructure to operate in proactive, rather than reactive, cyber security modes.