Jiwei Yan, Xi Deng, Ping Wang, Tianyong Wu, Jun Yan, Jian Zhang
{"title":"Characterizing and Identifying Misexposed Activities in Android Applications","authors":"Jiwei Yan, Xi Deng, Ping Wang, Tianyong Wu, Jun Yan, Jian Zhang","doi":"10.1145/3238147.3238164","DOIUrl":null,"url":null,"abstract":"Exported Activity (EA), a kind of activities in Android apps that can be launched by external components, is one of the most important inter-component communication (ICC) mechanisms to realize the interaction and cooperation among multiple apps. Existing works have pointed out that, once exposed, an activity will be vulnerable to malicious ICC attacks, such as permission leakage attack. Unfortunately, it is observed that a considerable number of activities in commercial apps are exposed inadvertently, while few works have studied the necessity and reasonability of such exposure. This work takes the first step to systematically study the exposing behavior of EAs through analyzing 13,873 Android apps. It utilizes the EA associated call relationships extracted from byte-code via data-flow analysis, as well as the launch conditions obtained from the manifest files, to guide the study on the usage and misexposure of EAs. The empirical findings are that the EA mechanism is widely adopted in development and the activities are liable to be misexposed due to the developers' misunderstanding or carelessness. Further study on subsets of apps selected according to different criteria indicates that the misexposed EAs have specific characteristics, which are manually summarized into six typical misuse patterns. As a consequence, ten heuristics are designed to decide whether an activity should be exposed or not and are implemented into an automatic tool called Mist. Experiments on the collected apps show that around one fifth EAs are unnecessarily exposed and there are more than one third EAs whose exposure may not be suggested.","PeriodicalId":6622,"journal":{"name":"2018 33rd IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"4 1","pages":"691-701"},"PeriodicalIF":0.0000,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 33rd IEEE/ACM International Conference on Automated Software Engineering (ASE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3238147.3238164","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8
Abstract
Exported Activity (EA), a kind of activities in Android apps that can be launched by external components, is one of the most important inter-component communication (ICC) mechanisms to realize the interaction and cooperation among multiple apps. Existing works have pointed out that, once exposed, an activity will be vulnerable to malicious ICC attacks, such as permission leakage attack. Unfortunately, it is observed that a considerable number of activities in commercial apps are exposed inadvertently, while few works have studied the necessity and reasonability of such exposure. This work takes the first step to systematically study the exposing behavior of EAs through analyzing 13,873 Android apps. It utilizes the EA associated call relationships extracted from byte-code via data-flow analysis, as well as the launch conditions obtained from the manifest files, to guide the study on the usage and misexposure of EAs. The empirical findings are that the EA mechanism is widely adopted in development and the activities are liable to be misexposed due to the developers' misunderstanding or carelessness. Further study on subsets of apps selected according to different criteria indicates that the misexposed EAs have specific characteristics, which are manually summarized into six typical misuse patterns. As a consequence, ten heuristics are designed to decide whether an activity should be exposed or not and are implemented into an automatic tool called Mist. Experiments on the collected apps show that around one fifth EAs are unnecessarily exposed and there are more than one third EAs whose exposure may not be suggested.