Li Li, Daoyuan Li, Tegawendé F. Bissyandé, Jacques Klein, Haipeng Cai, D. Lo, Yves Le Traon
{"title":"Automatically Locating Malicious Packages in Piggybacked Android Apps","authors":"Li Li, Daoyuan Li, Tegawendé F. Bissyandé, Jacques Klein, Haipeng Cai, D. Lo, Yves Le Traon","doi":"10.1109/MOBILESoft.2017.6","DOIUrl":null,"url":null,"abstract":"To devise efficient approaches and tools for detecting malicious packages in the Android ecosystem, researchers are increasingly required to have a deep understanding of malware. There is thus a need to provide a framework for dissecting malware and locating malicious program fragments within app code in order to build a comprehensive dataset of malicious samples. Towards addressing this need, we propose in this work a tool-based approach called HookRanker, which provides ranked lists of potentially malicious packages based on the way malware behaviour code is triggered. With experiments on a ground truth set of piggybacked apps, we are able to automatically locate the malicious packages from piggybacked Android apps with an accuracy of 83.6% in verifying the top five reported items.","PeriodicalId":281934,"journal":{"name":"2017 IEEE/ACM 4th International Conference on Mobile Software Engineering and Systems (MOBILESoft)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"29","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE/ACM 4th International Conference on Mobile Software Engineering and Systems (MOBILESoft)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MOBILESoft.2017.6","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 29
Abstract
To devise efficient approaches and tools for detecting malicious packages in the Android ecosystem, researchers are increasingly required to have a deep understanding of malware. There is thus a need to provide a framework for dissecting malware and locating malicious program fragments within app code in order to build a comprehensive dataset of malicious samples. Towards addressing this need, we propose in this work a tool-based approach called HookRanker, which provides ranked lists of potentially malicious packages based on the way malware behaviour code is triggered. With experiments on a ground truth set of piggybacked apps, we are able to automatically locate the malicious packages from piggybacked Android apps with an accuracy of 83.6% in verifying the top five reported items.