{"title":"对具有羊群行为的决策融合的最大熵攻击","authors":"Yiqing Lin;H. Vicky Zhao","doi":"10.1109/LSP.2024.3457244","DOIUrl":null,"url":null,"abstract":"The reliability and security of distributed detection systems have become increasingly important due to their growing prevalence in various applications. As advancements in human-machine systems continue, human factors, such as herding behaviors, are becoming influential in decision fusion process of these systems. The presence of malicious users further highlights the necessity to mitigate security concerns. In this paper, we propose a maximum entropy attack exploring the herding behaviors of users to amplify the hazard of attackers. Different from prior works that try to maximize the fusion error rate, the proposed attack maximizes the entropy of inferred system states from the fusion center, making the fusion results the same as a random coin toss. Moreover, we design static and dynamic attack modes to maximize the entropy of fusion results at the steady state and during the dynamic evolution stage, respectively. Simulation results show that the proposed attack strategy can cause the fusion accuracy to hover around 50% and existing fusion rules cannot resist our proposed attack, demonstrating its effectiveness.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":null,"pages":null},"PeriodicalIF":3.2000,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Maximum Entropy Attack on Decision Fusion With Herding Behaviors\",\"authors\":\"Yiqing Lin;H. Vicky Zhao\",\"doi\":\"10.1109/LSP.2024.3457244\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The reliability and security of distributed detection systems have become increasingly important due to their growing prevalence in various applications. As advancements in human-machine systems continue, human factors, such as herding behaviors, are becoming influential in decision fusion process of these systems. The presence of malicious users further highlights the necessity to mitigate security concerns. In this paper, we propose a maximum entropy attack exploring the herding behaviors of users to amplify the hazard of attackers. Different from prior works that try to maximize the fusion error rate, the proposed attack maximizes the entropy of inferred system states from the fusion center, making the fusion results the same as a random coin toss. Moreover, we design static and dynamic attack modes to maximize the entropy of fusion results at the steady state and during the dynamic evolution stage, respectively. Simulation results show that the proposed attack strategy can cause the fusion accuracy to hover around 50% and existing fusion rules cannot resist our proposed attack, demonstrating its effectiveness.\",\"PeriodicalId\":13154,\"journal\":{\"name\":\"IEEE Signal Processing Letters\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2024-09-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Signal Processing Letters\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10670295/\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Signal Processing Letters","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10670295/","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Maximum Entropy Attack on Decision Fusion With Herding Behaviors
The reliability and security of distributed detection systems have become increasingly important due to their growing prevalence in various applications. As advancements in human-machine systems continue, human factors, such as herding behaviors, are becoming influential in decision fusion process of these systems. The presence of malicious users further highlights the necessity to mitigate security concerns. In this paper, we propose a maximum entropy attack exploring the herding behaviors of users to amplify the hazard of attackers. Different from prior works that try to maximize the fusion error rate, the proposed attack maximizes the entropy of inferred system states from the fusion center, making the fusion results the same as a random coin toss. Moreover, we design static and dynamic attack modes to maximize the entropy of fusion results at the steady state and during the dynamic evolution stage, respectively. Simulation results show that the proposed attack strategy can cause the fusion accuracy to hover around 50% and existing fusion rules cannot resist our proposed attack, demonstrating its effectiveness.
期刊介绍:
The IEEE Signal Processing Letters is a monthly, archival publication designed to provide rapid dissemination of original, cutting-edge ideas and timely, significant contributions in signal, image, speech, language and audio processing. Papers published in the Letters can be presented within one year of their appearance in signal processing conferences such as ICASSP, GlobalSIP and ICIP, and also in several workshop organized by the Signal Processing Society.