Multi-Agent AI Systems for Coordinated Threat Response Using Deep Q-Networks (DQN) and Swarm Intelligence
Shantha Visalakshi Upendran, M R Mohanraj, Shiny Malar F.R · 2025
The increasing sophistication of cyber threats has made traditional defense mechanisms insufficient for addressing complex, large-scale attacks. Multi-Agent AI systems, particularly those utilizing Deep Q-Networks (DQN) and Swarm Intelligence (SI), have emerged as promising solutions for coordinated threat response in dynamic and distributed environments. These systems allow multiple agents to autonomously detect, assess, and mitigate threats through decentralized decision-making, enhancing scalability and efficiency in cybersecurity. However, ensuring the robustness and adversarial resilience of these systems remains a critical challenge. This chapter explores the integration of reinforcement learning and bio-inspired algorithms to develop a resilient multi-agent defense framework capable of adapting to both known and unknown cyber threats. The study examines the potential of DQN for adaptive learning in cyber defense, the role of SI in facilitating cooperative agent behavior, and strategies for improving system resilience against adversarial manipulations. Performance evaluations demonstrate the effectiveness of the proposed approach in real-world threat scenarios, offering a new paradigm for autonomous and scalable cyber defense systems. The chapter provides insights into optimizing multi-agent AI systems for proactive, robust, and efficient cybersecurity in large-scale networks.