Hey, You, Get Off of My Market: Detecting Malicious Apps in Official and Alternative Android Markets

By Yajin Zhou, Zhi Wang, Wu Zhou, Xuxian Jiang. Network and Distributed System Security Symposium, 2012

917 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/e4f7e5a5886c06f3f84e1256941ceb3d149471bf

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Citations in the corpus

Why eve and mallory love android: an analysis of android SSL (in)security (2012, background): “The heuristics of DroidRanger [26] could be extended to detect the vulnerabilities uncovered in our work.”
Apposcopy: semantics-based detection of Android malware through static analysis (2014, methodology): “The DroidRanger tool [40] uses permission-based behavioral footprint to detect instances of known malware families.”
Amandroid: A Precise and General Inter-component Data Flow Analysis Framework for Security Vetting of Android Apps (2014, background): “There has been a large body of work reporting Android app security issues [36, 37], some of which use static analysis techniques [11, 14, 18, 19].”
DroidScope: Seamlessly Reconstructing the OS and Dalvik Semantic Views for Dynamic Android Malware Analysis (2012, background): “Indeed recent research has shown that malicious applications exist in both the official and unofficial marketplaces with a rate of 0.02% and 0.2% respectively [41].”
Machine Learning for Android Malware Detection Using Permission and API Calls (2013, background): “So AndroidManifest.xml provides firsthand information to understand the characteristics and security settings of the Apps.”
DREBIN: Effective and Explainable Detection of Android Malware in Your Pocket (2014, methodology): “While recent approaches, such as DroidRanger [40] and AppPlayground [29], support filtering such applications off these markets, they induce a run-time overhead that is prohibitive for directly protecting smartphones.”
MARVIN: Efficient and Comprehensive Mobile App Classification through Static and Dynamic Analysis (2015, methodology): “DroidRanger [43] combines static and dynamic analysis to perform a large-scale study of malware in several Android app markets.”
A Probabilistic Discriminative Model for Android Malware Detection with Decompiled Source Code (2015, background): “RiskRanker [7] and DroidRanger [15] focus on the task of finding malicious applications in various Android markets.”
A measurement study of google play (2014, background): “Much work has focused on the security of Android applications and the presence of malware in Google Play, but this work has focused on possible compromises of user data and privacy on the Android devices themselves [20, 22, 25, 44].”
A Machine Learning Approach to Android Malware Detection (2012, methodology): “A study of 204,040 Android applications conducted in 2011 found 211 malicious applications on the ofﬁcial Android market and alternative marketplaces [37].”
Measuring user confidence in smartphone security and privacy (2012, background): “Prior work has shown that malware is much less common on official marketplaces than on alternative sources [41].”