{"title":"MadeCR: Correlation-based malware detection for cognitive radio","authors":"Yanzhi Dou, K. Zeng, Yaling Yang, D. Yao","doi":"10.1109/INFOCOM.2015.7218432","DOIUrl":null,"url":null,"abstract":"Cognitive Radio (CR) is an intelligent radio technology to boost spectrum utilization and is likely to be widely spread in the near future. However, its flexible software-oriented design may be exploited by an adversary to control CR devices to launch large scale attacks on a wide range of critical wireless infrastructures. To proactively mitigate the potentially serious threat, this paper presents MadeCR, a Correlation-based Malware detection system for CR. MadeCR exploits correlations among CR applications' component actions to detect malicious behaviors. In addition, a significant contribution of the paper is a general experimentation method referred to as mutation testing to comprehensively evaluate the effectiveness of the anomaly detection method against a large number of artificial malware cases. Evaluation shows that MadeCR detects malicious behaviors within 1.10s at an accuracy of 94.9%.","PeriodicalId":342583,"journal":{"name":"2015 IEEE Conference on Computer Communications (INFOCOM)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE Conference on Computer Communications (INFOCOM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INFOCOM.2015.7218432","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 12
Abstract
Cognitive Radio (CR) is an intelligent radio technology to boost spectrum utilization and is likely to be widely spread in the near future. However, its flexible software-oriented design may be exploited by an adversary to control CR devices to launch large scale attacks on a wide range of critical wireless infrastructures. To proactively mitigate the potentially serious threat, this paper presents MadeCR, a Correlation-based Malware detection system for CR. MadeCR exploits correlations among CR applications' component actions to detect malicious behaviors. In addition, a significant contribution of the paper is a general experimentation method referred to as mutation testing to comprehensively evaluate the effectiveness of the anomaly detection method against a large number of artificial malware cases. Evaluation shows that MadeCR detects malicious behaviors within 1.10s at an accuracy of 94.9%.