A Unified Attack Detection Strategy for Multi-Agent Systems over Transient and Steady Stages
This paper proposes a unified detection strategy against three kinds of attacks for multi-agent systems (MASs) which is applicable to both transient and steady stages. For attacks on the communication layer, a watermarking-based detection scheme with KullbackLeibler (KL) divergence is designed. Different from traditional communication schemes, each agent transmits a message set containing two state values with different types of watermarking. It is found that the detection performance is determined by the relevant parameters of the watermarking signal. Unlike the existing detection manoeuvres, such a scheme is capable of transient and steady stages. For attacks on the agent layer, a convergence rate related detection approach is put forward. It is shown that the resilience of the considered system is characterized by the coefficient and offset of the envelope. For hybrid attacks, based on the above detection mechanisms, a general framework resorting to trusted agents is presented, which requires weaker graph conditions and less information transmission. Finally, an example associated with the platooning of connected vehicles is given to support the theoretical results.
Code (0)
등록된 구현이 없습니다.
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Distributed Detection and Mitigation of Biasing Attacks over Multi-Agent Networks
This paper proposes a distributed attack detection and mitigation technique based on distributed estimation over a multi-agent network, where the agents take partial system measurements susceptible to (possible) biasing …
Novel Defense Strategy Against Zero-Dynamics Attack in Multi-Agent Systems
This paper considers the defense strategy of strategic topology switching for the second-order multi-agent system under zero-dynamics attack (ZDA) whose attack-starting time is allowed to be not the initial time. We firs…
BARD-MARL: Byzantine-Agent Detection for Learned Communication in Multi-Agent Reinforcement Learning
Learned communication improves coordination in cooperative multi-agent reinforcement learning, but it also creates a trust problem: a trained policy may route information through agents that have become faulty or adversa…
Multi-agent Reinforcement LearningMEMSAD: Gradient-Coupled Anomaly Detection for Memory Poisoning in Retrieval-Augmented Agents
Persistent external memory enables LLM agents to maintain context across sessions, yet its security properties remain formally uncharacterized. We formalize memory poisoning attacks on retrieval-augmented agents as a Sta…
Anomaly DetectionUniGuardian: A Unified Defense for Detecting Prompt Injection, Backdoor Attacks and Adversarial Attacks in Large Language Models
Large Language Models (LLMs) are vulnerable to attacks like prompt injection, backdoor attacks, and adversarial attacks, which manipulate prompts or models to generate harmful outputs. In this paper, departing from tradi…
Text Generation