TY - GEN
T1 - Demystifying Diehard Android Apps
AU - Zhou, Hao
AU - Wang, Haoyu
AU - Zhou, Yajin
AU - Luo, Xiapu
AU - Tang, Yutian
AU - Xue, Lei
AU - Wang, Ting
N1 - Funding Information:
We thank the anonymous reviewers for their helpful comments. This research is partially supported by the Hong Kong RGC Projects (No. 152279/16E, 152223/17E, CityU C1008-16G) and the National Natural Science Foundation of China (No. 61702045, No. 61872438) and Leading Innovative and Entrepreneur Team Introduction Program of Zhejiang (2018R01005) and Zhejiang Key R&D (2019C03133) and the National Science Foundation under Grant (No. 1953893, 1953813, and 1951729).
Publisher Copyright:
© 2020 ACM.
PY - 2020/9
Y1 - 2020/9
N2 - Smartphone vendors are using multiple methods to kill processes of Android apps to reduce the battery consumption. This motivates developers to find ways to extend the liveness time of their apps, hence the name diehard apps in this paper. Although there are blogs and articles illustrating methods to achieve this purpose, there is no systematic research about them. What's more important, little is known about the prevalence of diehard apps in the wild. In this paper, we take a first step to systematically investigate diehard apps by answering the following research questions. First, why and how can they circumvent the resource-saving mechanisms of Android? Second, how prevalent are they in the wild? In particular, we conduct a semi -automated analysis to illustrate insights why existing methods to kill app processes could be evaded, and then systematically present 12 diehard methods. After that, we develop a system named DiehardDetector to detect diehard apps in a large scale. The experimental result of applying DiehardDetector to more than 80k Android apps downloaded from Google Play showed that around 21 % of apps adopt various diehard methods. Moreover, our system can achieve high precision and recall.
AB - Smartphone vendors are using multiple methods to kill processes of Android apps to reduce the battery consumption. This motivates developers to find ways to extend the liveness time of their apps, hence the name diehard apps in this paper. Although there are blogs and articles illustrating methods to achieve this purpose, there is no systematic research about them. What's more important, little is known about the prevalence of diehard apps in the wild. In this paper, we take a first step to systematically investigate diehard apps by answering the following research questions. First, why and how can they circumvent the resource-saving mechanisms of Android? Second, how prevalent are they in the wild? In particular, we conduct a semi -automated analysis to illustrate insights why existing methods to kill app processes could be evaded, and then systematically present 12 diehard methods. After that, we develop a system named DiehardDetector to detect diehard apps in a large scale. The experimental result of applying DiehardDetector to more than 80k Android apps downloaded from Google Play showed that around 21 % of apps adopt various diehard methods. Moreover, our system can achieve high precision and recall.
KW - n/a
UR - http://www.scopus.com/inward/record.url?scp=85097171622&partnerID=8YFLogxK
U2 - 10.1145/3324884.3416637
DO - 10.1145/3324884.3416637
M3 - Conference article published in proceeding or book
AN - SCOPUS:85097171622
T3 - Proceedings - 2020 35th IEEE/ACM International Conference on Automated Software Engineering, ASE 2020
SP - 187
EP - 198
BT - Proceedings - 2020 35th IEEE/ACM International Conference on Automated Software Engineering, ASE 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 35th IEEE/ACM International Conference on Automated Software Engineering, ASE 2020
Y2 - 22 September 2020 through 25 September 2020
ER -