{"title":"Poster: May the Swarm Be With You: Sensor Spoofing Attacks Against Drone Swarms","authors":"Yingao Yao, Pritam Dash, K. Pattabiraman","doi":"10.1145/3548606.3563535","DOIUrl":null,"url":null,"abstract":"Swarm robotics, particularly drone swarms, are used in various safety-critical tasks. While a lot of attention has been paid to improving swarm control algorithms for improved intelligence, the security implications of various design choices in swarm control algorithms have not been studied. We highlight how an attacker can exploit the vulnerabilities in swarm control algorithms to disrupt drone swarms. Specifically, we show that the attacker can target one swarm member (target drone) through sensor spoofing attacks, and indirectly cause other swarm members (victim drones) to veer off from their course, and potentially resulting in a crash. Our attack cannot be prevented by traditional software security techniques, and it is stealthy in nature as it causes seemingly benign deviations in drone swarms. Our initial results show that spoofing the position of a target drone by 5m is sufficient to cause other drones to crash into a front obstacle. Overall, our attack achieves 76.67% and 93.33% success rate with 5m and 10m spoofing deviation respectively.","PeriodicalId":435197,"journal":{"name":"Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security","volume":"37 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3548606.3563535","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
Swarm robotics, particularly drone swarms, are used in various safety-critical tasks. While a lot of attention has been paid to improving swarm control algorithms for improved intelligence, the security implications of various design choices in swarm control algorithms have not been studied. We highlight how an attacker can exploit the vulnerabilities in swarm control algorithms to disrupt drone swarms. Specifically, we show that the attacker can target one swarm member (target drone) through sensor spoofing attacks, and indirectly cause other swarm members (victim drones) to veer off from their course, and potentially resulting in a crash. Our attack cannot be prevented by traditional software security techniques, and it is stealthy in nature as it causes seemingly benign deviations in drone swarms. Our initial results show that spoofing the position of a target drone by 5m is sufficient to cause other drones to crash into a front obstacle. Overall, our attack achieves 76.67% and 93.33% success rate with 5m and 10m spoofing deviation respectively.