MULTI-AGENT DEFENSE SYSTEMS AND THEIR EFFECTIVENESS EVALUATION
DOI:
https://doi.org/10.5281/zenodo.17436770Keywords:
Multi-Agent Systems (MAS), Distributed Cybersecurity Architecture, Intrusion Detection and Prevention Systems (IDS/IPS), IoT Security, Real-Time Threat Response, Autonomous Security Agents, Federated Learning in Cyber Defense, Artifcial Intelligence for Network Security, Resilient System Design, Decentralized Decision-Making.Abstract
This paper explores the effectiveness of Multi-Agent Systems (MAS) in modern cybersecurity infrastructures.
As traditional centralized models struggle to address dynamic and complex threats, MAS provides a distributed and
autonomous alternative. The study evaluates real-world MAS implementations such as Suricata, HoneyMesh, Google
DC Agents, AWS IoT Defender, Azure Sphere, Tesla FSD, and IBM QRadar-Watson using three key indicators: response
time, detection accuracy, and overall efciency.
Findings show that agent-based architectures enhance system resilience, enable real-time threat analysis, and mitigate
single points of failure. Integration with artifcial intelligence (AI) and federated learning further improves predictive
capabilities. MAS also supports proactive defense, adaptive coordination, and efcient resource utilization, making them
ideal for securing IoT environments, edge computing systems, and future 6G networks. The results highlight MAS as a
strategic solution for building flexible, scalable, and intelligent cybersecurity frameworks capable of addressing evolving
digital threats.
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