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This research proposes a decentralized multi-agent swarm architecture for securing Industrial IoT grids by replacing centralized security models with autonomous edge agents that coordinate via peer-to-peer consensus. The approach demonstrates significant performance gains, including sub-millisecond threat response times and reduced bandwidth usage compared to traditional cloud-dependent solutions. By enabling local detection and quarantine of attacks without latency bottlenecks, this system addresses critical scalability challenges in large-scale manufacturing ecosystems where speed is essential for defense against sophisticated threats.
Decentralized Multi-Agent Swarms for Autonomous Grid Security in Industrial IoT: A Consensus-based Approach
arXiv:2601.17303v2 Announce Type: replace-cross Abstract: As Industrial Internet of Things (IIoT) environments scale to tens of thousands of connected devices, centralized security architectures introduce latency bottlenecks that sophisticated attackers can exploit to compromise an entire manufacturing ecosystem. We present a Decentralized Multi-Agent Swarm (DMAS) architecture that deploys autonomous agents at each edge gateway, forming a distributed defense layer for IIoT networks. Rather than relying on static firewalls or cloud-forwarded telemetry, DMAS agents coordinate through a lightweight peer-to-peer protocol, detecting threats locally without cloud dependency. We describe a Consensus-based Threat Validation (CVT) protocol in which agents collectively vote on detected threats, enabling near-instant quarantine of compromised nodes. Experiments on a 2000-device hardware testbed show that DMAS achieves sub-millisecond response times (0.85 ms average), 97.3% detection accuracy under high load, and 87% accuracy on zeroday attacks, each exceeding both centralized and edge-computing baselines. Bandwidth consumption drops by 89% relative to cloud-based solutions.
Abstract:As Industrial Internet of Things (IIoT) environments scale to tens of thousands of connected devices, centralized security architectures introduce latency bottlenecks that sophisticated attackers can exploit to compromise an entire manufacturing ecosystem. We present a Decentralized Multi-Agent Swarm (DMAS) architecture that deploys autonomous agents at each edge gateway, forming a distributed defense layer for IIoT networks. Rather than relying on static firewalls or cloud-forwarded telemetry, DMAS agents coordinate through a lightweight peer-to-peer protocol, detecting threats locally without cloud dependency. We describe a Consensus-based Threat Validation (CVT) protocol in which agents collectively vote on detected threats, enabling near-instant quarantine of compromised nodes. Experiments on a 2000-device hardware testbed show that DMAS achieves sub-millisecond response times (0.85 ms average), 97.3% detection accuracy under high load, and 87% accuracy on zeroday attacks, each exceeding both centralized and edge-computing baselines. Bandwidth consumption drops by 89% relative to cloud-based solutions.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC); Emerging Technologies (cs.ET)
arXiv:2601.17303 [cs.LG]
(or arXiv:2601.17303v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2601.17303 Focus to learn more arXiv-issued DOI via DataCite
https://doi.org/10.1109/MCNA69896.2026.11574290 Focus to learn more DOI(s) linking to related resources
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