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20132026
most citedApplication of Deep Reinforcement Learning for Intrusion Detection in Internet of Things: A Systematic Review

48 citations · 117 across the 21 of their papers we have counts for

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13 papers · 1 filter

cs.CR2026

Swarm-Driven Multi-Agent Reasoning for Smart City Security

Saeid Jamshidi, Kawser Wazed Nafi, Carol Fung +1

Modern smart cities are interconnected cyber-physical ecosystems where heterogeneous devices exchange data and control commands. Coordinated attacks may appear as weak and distribu…

cs.CR2026

Security Engineering of OpenClaw: Analyzing Attack Surface Expansion and Trust-Boundary Violations

Saeid Jamshidi, Arghavan Moradi Dakhel, Kawser Wazed Nafi +1

Agentic large language model (LLM) systems can now execute actions, not only produce text. When model outputs trigger privileged operations such as shell commands, browser automati…

cs.CR2026

Game-Theoretic Multi-Agent Control for Robust Contextual Reasoning in LLMs

Saeid Jamshidi, Amin Nikanjam, Arghavan Moradi Dakhel +2

Large Language Models (LLMs) in multi-turn interactions maintain evolving context rather than generating isolated responses, making them vulnerable to prompt-injection and context-…

cs.CR2026

Hallucination Cascade: Analyzing Error Propagation in Multi-Agent LLM Systems

Saeid Jamshidi, Arghavan Moradi Dakhel, Kawser Wazed Nafi +1

Large Language Models (LLMs) generate fluent text but remain vulnerable to hallucinations, producing unsupported, inconsistent, and factually incorrect claims. Most prior work trea…

cs.CR2026

Self-Adaptive Multi-Agent LLM-Based Security Pattern Selection for IoT Systems

Saeid Jamshidi, Foutse Khomh, Carol Fung +1

The adoption of Internet of Things (IoT) systems at the network edge of smart architectures is increasing rapidly, intensifying the need for security mechanisms that are both adapt…

cs.CR2026★ 1 cited

Tri-LLM Cooperative Federated Zero-Shot Intrusion Detection with Semantic Disagreement and Trust-Aware Aggregation

Saeid Jamshidi, Omar Abdul Wahab, Foutse Khomh +1

Federated learning (FL) has become an effective paradigm for privacy-preserving, distributed Intrusion Detection Systems (IDS) in cyber-physical and Internet of Things (IoT) networ…