Robust Sensor Localization against Known Sensor Position Attacks

作     者：Won, Jongho Bertino, Elise 

作者机构：Purdue Univ Dept Comp Sci W Lafayette IN 47907 USA 

出 版 物：《IEEE TRANSACTIONS ON MOBILE COMPUTING》 (IEEE移动计算汇刊)

年 卷 期：2019年第18卷第12期

页      面：2954-2967页

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Purdue Cyber Center National Science Foundation [CNS-1111512] 

主　　题：Robot sensing systems Global Positioning System Mobile computing Wireless sensor networks Estimation Receivers Security Secure localization sensor localization MMSE known sensor position attacks beacon deployment 

摘      要：Many localization techniques for wireless sensor networks have been proposed to estimate sensor positions even when there is malicious location information which distorts the estimate. Some of those solutions efficiently estimate sensor positions using the Minimum Mean Square Estimation methods and the Degree-Of-Consistency (DOC). Although such solutions can filter out most of malicious information, if an attacker knows the position of the target sensor, the attacker can significantly alter its estimated position. In this paper, we introduce two attacks based on the knowledge of target sensor positions. The first attack, called Aligned-Beacon-Position (ABP) attack, exploits beacon nodes that are aligned in a line. The second attack, called inside-attack, disables the DOC filtering algorithm by placing malicious location references inside benign location references. To protect against those two known sensor position attacks, we introduce two defense schemes. First, we propose a novel beacon placement strategy to protect against ABP attacks. Second, we present a filtering technique that can filter out malicious location references introduced by inside-attacks. Finally, we propose a localization algorithm which has enhanced accuracy and efficiency compared with previous algorithms. We assess the impact of those attacks and the performance of our defense schemes by simulation and test-bed experiments.