Swarm-Driven Multi-Agent Reasoning for Smart City Security

2026-07-03

AI Summary

This study addresses the challenge of detecting coordinated cyberattacks in smart cities, where attack signals are typically weak, spatially distributed, and fall below local alert thresholds, rendering conventional anomaly detection methods ineffective. To overcome this limitation, the authors propose the TPSC-Sec framework, which employs multiple specialized large language model agents to separately model traffic behavior, protocol interactions, identity usage, and attack temporal patterns. The framework introduces a novel threat pheromone–based swarm consensus mechanism that integrates adaptive validation calibration, context-sensitive weighting, and divergence control to achieve highly robust security reasoning. Experimental results demonstrate that the approach attains a consensus acceptance rate of 0.97 ± 0.02 and hypothesis concentration exceeding 0.99 across 500 trials, with an aggregated risk as low as 0.23 ± 0.04, while simultaneously improving system fitness by 11.6% despite a 50% reduction in agent count.

Abstract

Modern smart cities are interconnected cyber-physical ecosystems where heterogeneous devices exchange data and control commands. Coordinated attacks may appear as weak and distributed indicators, including low-rate scanning, abnormal credential use, protocol misuse, or delayed lateral movement, with each signal remaining below local alert thresholds. Therefore, smart-city security is not only an anomaly detection task but also a reasoning task under uncertainty, partial observability, and adversarial manipulation. This work presents TPSC-Sec, an LLM-based multi-agent approach for stable security reasoning in smart cities. TPSC-Sec decomposes analysis across specialized agents that inspect traffic behavior, protocol interactions, identity usage, and temporal attack progression. Their independent threat hypotheses are aggregated by the proposed Threat-Pheromone Swarm Consensus mechanism, which reinforces supported hypotheses, suppresses contradictions, and preserves temporal consistency, thereby driving competing interpretations toward a stable collective decision. We further introduce Adaptive Verified TPSC, which adds verification-aware calibration, context-sensitive weighting, and disagreement-adaptive control to reduce unsupported LLM outputs and reasoning inconsistency. Experiments over 500 runs show that TPSC-Sec achieves a high consensus acceptance rate of 0.97 plus or minus 0.02, hypothesis-support concentration above 0.99, a consensus margin of 2.08 plus or minus 0.21, low aggregate risk of 0.23 plus or minus 0.04, high inter-agent agreement of 0.82 plus or minus 0.06, and strong support-quality correlation of r equals 0.93. Adaptive agent selection reduces the number of active agents by 50 percent while improving system fitness by 11.6 percent. These results demonstrate robust, interpretable, and efficient security reasoning for adversary-resilient smart-city environments.

Problem

• smart city security
• multi-agent reasoning
• adversarial attacks
• anomaly detection
• partial observability

Innovation

• multi-agent reasoning
• swarm consensus
• LLM-based security
• adaptive verification
• smart city defense