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

48 citations · 82 across the 21 of their papers we have counts for

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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

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

Saeid Jamshidi

The rapid proliferation of Internet of Things (IoT) devices has enabled unprecedented automation and connectivity, but it has also substantially increased the attack surface, expos…

cs.CR2026

SGTO-MAS: Secure Gorilla Troops Optimization for Multi-Agent LLM Systems

Saeid Jamshidi

Multi-agent large language model (LLM) systems offer strong capabilities for complex reasoning and decision-making, yet coordination across agents introduces error propagation, sec…

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…