A Synthesis of AI/ML Algorithms and Multi-Agent Systems for Next-Generation IoT Cyber Defense

Abstract

The rapid growth of the IoT has brought up a massive network of inter-connected devices, operating across domestic, industrial, and urban environments. While these systems provide greatly improved levels of automation, efficiency, and real-time access to data, their mass deployment presents significant cybersecurity challenges in return [cite: 1570]. Most IoT devices suffer from weak computational and security capabilities and thus are prone to various attacks, such as hijacking, malware propagation, and DDoS attacks. Traditional security architectures based on a centralized paradigm tend to become increasingly inadequate for responding dynamically and on a large scale to the IoT-related cyber-threats due to latency issues, scaling, and single-point-of-failure problems. In this regard, Agent-Based Artificial Intelligence appears to be an effective defense approach with a decentralized attitude. With this approach, autonomous intelligent agents would be situated at the network level, monitoring current behavior, detecting anomalies, cooperating with other agents, and responding in real- time to intrusion. The properties of adaptiveness, distribution, and cooperation encapsulated in agent-based AI enable faster threat identification, greater resilience, and continuous learning in targeted systems. This paper examines the role that agent- based AI can play in cyber-defense for IoT systems, discusses sys- tem architectures and coordination mechanisms, and addresses their advantages and challenges with respect to more traditional models of security. Its findings support the consideration of agent- based AI as an important framework through which the security and dependability of future IoT ecosystems can be improved.