{"title":"合谋数据泄露等:应用间通信的大规模威胁分析","authors":"Amiangshu Bosu, Fang Liu, D. Yao, G. Wang","doi":"10.1145/3052973.3053004","DOIUrl":null,"url":null,"abstract":"Inter-Component Communication (ICC) provides a message passing mechanism for data exchange between Android applications. It has been long believed that inter-app ICCs can be abused by malware writers to launch collusion attacks using two or more apps. However, because of the complexity of performing pairwise program analysis on apps, the scale of existing analyses is too small (e.g., up to several hundred) to produce concrete security evidence. In this paper, we report our findings in the first large-scale detection of collusive and vulnerable apps, based on inter-app ICC data flows among 110,150 real-world apps. Our system design aims to balance the accuracy of static ICC resolution/data-flow analysis and run-time scalability. This large-scale analysis provides real-world evidence and deep insights on various types of inter-app ICC abuse. Besides the empirical findings, we make several technical contributions, including a new open-source ICC resolution tool with improved accuracy over the state-of-the-art, and a large database of inter-app ICCs and their attributes.","PeriodicalId":20540,"journal":{"name":"Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security","volume":"18 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2017-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"124","resultStr":"{\"title\":\"Collusive Data Leak and More: Large-scale Threat Analysis of Inter-app Communications\",\"authors\":\"Amiangshu Bosu, Fang Liu, D. Yao, G. Wang\",\"doi\":\"10.1145/3052973.3053004\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Inter-Component Communication (ICC) provides a message passing mechanism for data exchange between Android applications. It has been long believed that inter-app ICCs can be abused by malware writers to launch collusion attacks using two or more apps. However, because of the complexity of performing pairwise program analysis on apps, the scale of existing analyses is too small (e.g., up to several hundred) to produce concrete security evidence. In this paper, we report our findings in the first large-scale detection of collusive and vulnerable apps, based on inter-app ICC data flows among 110,150 real-world apps. Our system design aims to balance the accuracy of static ICC resolution/data-flow analysis and run-time scalability. This large-scale analysis provides real-world evidence and deep insights on various types of inter-app ICC abuse. Besides the empirical findings, we make several technical contributions, including a new open-source ICC resolution tool with improved accuracy over the state-of-the-art, and a large database of inter-app ICCs and their attributes.\",\"PeriodicalId\":20540,\"journal\":{\"name\":\"Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security\",\"volume\":\"18 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-04-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"124\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3052973.3053004\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3052973.3053004","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Collusive Data Leak and More: Large-scale Threat Analysis of Inter-app Communications
Inter-Component Communication (ICC) provides a message passing mechanism for data exchange between Android applications. It has been long believed that inter-app ICCs can be abused by malware writers to launch collusion attacks using two or more apps. However, because of the complexity of performing pairwise program analysis on apps, the scale of existing analyses is too small (e.g., up to several hundred) to produce concrete security evidence. In this paper, we report our findings in the first large-scale detection of collusive and vulnerable apps, based on inter-app ICC data flows among 110,150 real-world apps. Our system design aims to balance the accuracy of static ICC resolution/data-flow analysis and run-time scalability. This large-scale analysis provides real-world evidence and deep insights on various types of inter-app ICC abuse. Besides the empirical findings, we make several technical contributions, including a new open-source ICC resolution tool with improved accuracy over the state-of-the-art, and a large database of inter-app ICCs and their attributes.