Modeling and Implementation of Self-Defending Infrastructure Systems Using AI-Driven Security Controls

Abstract

The increasing dependence on large-scale and interconnected infrastructure systems has significantly intensified security challenges, as traditional rule-based and reactive defense mechanisms are no longer sufficient to counter sophisticated and evolving cyber threats. Critical infrastructures such as communication networks, cloud platforms, and cyber-physical systems require intelligent, adaptive, and autonomous security solutions capable of ensuring resilience and continuous operation. In this context, self-defending infrastructure systems enabled by artificial intelligence (AI) have emerged as a promising approach for proactive threat detection and response. This paper presents the modeling and implementation of a self-defending infrastructure system using AI-driven security controls. The proposed approach integrates continuous data collection, intelligent threat analysis, adaptive decision-making, and automated response mechanisms within a unified and scalable architecture. Machine learning and deep learning techniques are employed to identify anomalous and malicious behaviors, while reinforcement learning is used to optimize response strategies based on environmental feedback. The system is designed to operate in real time and adapt dynamically to changing threat conditions with minimal human intervention. The methodology is evaluated using a controlled experimental testbed that simulates realistic infrastructure environments and diverse cyber-attack scenarios, including denial-of-service attacks, unauthorized access attempts, and malware activities. Experimental results demonstrate that the proposed system achieves higher detection accuracy, lower false positive rates, and faster response times compared to traditional security approaches, while maintaining acceptable system overhead.