Exploring Permission-Induced Risk in Android Applications for Malicious Application Detection

Wei Wang, Xing Wang, Dawei Feng, Jiqiang Liu, Zhen Han, Xiangliang Zhang

Research output: Contribution to journalArticlepeer-review

301 Scopus citations


Android has been a major target of malicious applications (malapps). How to detect and keep the malapps out of the app markets is an ongoing challenge. One of the central design points of Android security mechanism is permission control that restricts the access of apps to core facilities of devices. However, it imparts a significant responsibility to the app developers with regard to accurately specifying the requested permissions and to the users with regard to fully understanding the risk of granting certain combinations of permissions. Android permissions requested by an app depict the app's behavioral patterns. In order to help understanding Android permissions, in this paper, we explore the permission-induced risk in Android apps on three levels in a systematic manner. First, we thoroughly analyze the risk of an individual permission and the risk of a group of collaborative permissions. We employ three feature ranking methods, namely, mutual information, correlation coefficient, and T-test to rank Android individual permissions with respect to their risk. We then use sequential forward selection as well as principal component analysis to identify risky permission subsets. Second, we evaluate the usefulness of risky permissions for malapp detection with support vector machine, decision trees, as well as random forest. Third, we in depth analyze the detection results and discuss the feasibility as well as the limitations of malapp detection based on permission requests. We evaluate our methods on a very large official app set consisting of 310 926 benign apps and 4868 real-world malapps and on a third-party app sets. The empirical results show that our malapp detectors built on risky permissions give satisfied performance (a detection rate as 94.62% with a false positive rate as 0.6%), catch the malapps' essential patterns on violating permission access regulations, and are universally applicable to unknown malapps (detection rate as 74.03%).
Original languageEnglish (US)
Pages (from-to)1869-1882
Number of pages14
JournalIEEE Transactions on Information Forensics and Security
Issue number11
StatePublished - Sep 8 2014

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01


Dive into the research topics of 'Exploring Permission-Induced Risk in Android Applications for Malicious Application Detection'. Together they form a unique fingerprint.

Cite this