Multi-Rate Sampled-Data Secure Fusion Estimation Against Malicious Hybrid Attacks

IF 3 3区计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-04-09 DOI:10.1109/TSIPN.2025.3559434

Haiyu Song;Siqing Ye;Peng Shi;Wen-An Zhang;Li Yu

{"title":"Multi-Rate Sampled-Data Secure Fusion Estimation Against Malicious Hybrid Attacks","authors":"Haiyu Song;Siqing Ye;Peng Shi;Wen-An Zhang;Li Yu","doi":"10.1109/TSIPN.2025.3559434","DOIUrl":null,"url":null,"abstract":"This paper investigates the Kalman fusion estimation problem for multi-sensor systems based on multi-rate sampled data within a non-secure network environment. For each sensor, an innovative multi-rate sampling estimation module is proposed, allowing for multiple samplings within a single estimation cycle to gather as much sampled information as possible. The sampled data during transmission is thought to encounter three potential scenarios: being subjected to DoS attack, FDI attack, or undergoing normal transmission. These three potential scenarios are modeled as a random phenomenon described by two sets of Bernoulli variables. A unified information framework is subsequently introduced, adept at encompassing the three attack scenarios along with the multi-rate sampling process. This framework serves as the basis for the design of a local secure Kalman estimator, followed by stability analysis. Finally, a distributed secure fusion estimation algorithm is proposed, and its effectiveness is demonstrated through a simulation example.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"401-412"},"PeriodicalIF":3.0000,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Signal and Information Processing over Networks","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10960373/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}

引用次数: 0

Abstract

This paper investigates the Kalman fusion estimation problem for multi-sensor systems based on multi-rate sampled data within a non-secure network environment. For each sensor, an innovative multi-rate sampling estimation module is proposed, allowing for multiple samplings within a single estimation cycle to gather as much sampled information as possible. The sampled data during transmission is thought to encounter three potential scenarios: being subjected to DoS attack, FDI attack, or undergoing normal transmission. These three potential scenarios are modeled as a random phenomenon described by two sets of Bernoulli variables. A unified information framework is subsequently introduced, adept at encompassing the three attack scenarios along with the multi-rate sampling process. This framework serves as the basis for the design of a local secure Kalman estimator, followed by stability analysis. Finally, a distributed secure fusion estimation algorithm is proposed, and its effectiveness is demonstrated through a simulation example.

查看原文本刊更多论文

针对恶意混合攻击的多速率采样数据安全融合估计

研究了非安全网络环境下基于多速率采样数据的多传感器系统的卡尔曼融合估计问题。对于每个传感器，提出了一种创新的多速率采样估计模块，允许在单个估计周期内进行多次采样，以收集尽可能多的采样信息。采样数据在传输过程中可能遇到三种情况：遭到DoS攻击、FDI攻击或正常传输。这三种可能的情况被建模为由两组伯努利变量描述的随机现象。随后，引入了统一的信息框架，擅长于包含三种攻击场景以及多速率采样过程。该框架为局部安全卡尔曼估计的设计和稳定性分析提供了基础。最后，提出了一种分布式安全融合估计算法，并通过仿真实例验证了该算法的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

IEEE Transactions on Signal and Information Processing over Networks Computer Science-Computer Networks and Communications

CiteScore

5.80

自引率

12.50%

发文量

期刊介绍： The IEEE Transactions on Signal and Information Processing over Networks publishes high-quality papers that extend the classical notions of processing of signals defined over vector spaces (e.g. time and space) to processing of signals and information (data) defined over networks, potentially dynamically varying. In signal processing over networks, the topology of the network may define structural relationships in the data, or may constrain processing of the data. Topics include distributed algorithms for filtering, detection, estimation, adaptation and learning, model selection, data fusion, and diffusion or evolution of information over such networks, and applications of distributed signal processing.