Semantic Multi-Agent Intrusion Detection for IoT:Zero-Day and Adversarial Threats with Risk-Aware Reasoning

• URL: http://arxiv.org/abs/2606.10323v1
• Date: Tue, 09 Jun 2026 02:18:50 GMT
• Title: Semantic Multi-Agent Intrusion Detection for IoT:Zero-Day and Adversarial Threats with Risk-Aware Reasoning
• Abstract summary: Internet of Things (IoT) devices have exposed networks to sophisticated cyber threats, including zero-day and adversarial intrusions.<n>Traditional Intrusion Detection Systems (IDS) struggle to generalize to unseen attacks, often require substantial computational resources, and lack interpretability.<n>Recent advances, including Deep Learning (DL), open-set detection, and Large Language Model (LLM)-based semantic reasoning, address some of these challenges but typically focus on zero-day and adversarial threats.<n>We propose a semantic multi-agent ID that integrates four specialized agents (e.g., Scout, Mutator, Auditor, and Arbiter
• Score: 0.5076419064097734
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid proliferation of Internet of Things (IoT) devices has enabled unprecedented automation and connectivity, but it has also substantially increased the attack surface, exposing networks to sophisticated cyber threats, including zero-day and adversarial intrusions. Traditional Intrusion Detection Systems (IDS) struggle to generalize to unseen attacks, often require substantial computational resources, and lack interpretability, particularly in resource-constrained and heterogeneous IoT networks. Recent advances, including Deep Learning (DL), open-set detection, and Large Language Model (LLM)-based semantic reasoning, address some of these challenges but typically focus on zero-day and adversarial threats and rarely combine semantic reasoning with multi-agent systems. To overcome these limitations, we propose a semantic multi-agent ID that integrates four specialized agents (e.g., Scout, Mutator, Auditor, and Arbiter) that leverage semantic embeddings and multi-stage probabilistic decision fusion. The Scout induces structured hypotheses from semantic embeddings; the Mutator generates adversarially constrained variants; the Auditor evaluates consistency and filters unreliable outputs; and the Arbiter produces interpretable, risk-aware alerts. Extensive experiments across multiple real-world IoT datasets demonstrate that the proposed system achieves 95.9% overall detection accuracy, reduces false-positive rates to 6.8%, improves zero-day detection to 87.9%, and maintains computational efficiency suitable for edge deployment.

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