Detecting repackaged smartphone applications in third-party android marketplaces

By Wu Zhou, Yajin Zhou, Xuxian Jiang, Peng Ning. Conference on Data and Application Security and Privacy, 2012

677 citations recorded by the source corpus.

Subfields: Information Systems, Software, Signal Processing

View Paper: https://www.semanticscholar.org/paper/bd0a1acb9d90432d4cbb4a70a94326906d9595ca

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Systems

1. Michael Backes
2. Sven Bugiel
3. Erik Derr
4. Lorenzo Cavallaro
5. Kimberly Tam
6. Rosli Bin Salleh
7. Nor Badrul Anuar
8. Ali Feizollah

In Software

1. Michael Backes
2. Sven Bugiel
3. Erik Derr
4. Kai Chen
5. Peng Liu
6. Nicolas Viennot
7. Jason Nieh
8. Yingjun Zhang

In Signal Processing

1. Michael Backes
2. Sven Bugiel
3. Erik Derr
4. Lorenzo Cavallaro
5. Kimberly Tam
6. Nor Badrul Anuar
7. Ali Feizollah
8. Rosli Bin Salleh

Citations in the corpus

• A Multimodal Deep Learning Method for Android Malware Detection Using Various Features (2019, methodology): “DroidMoss [9] is a system that uses a fuzzy hashing technique to measure similarities of applications to detect the repacked malware.”
• User feedback in the appstore: An empirical study (2013, background): “[19] investigated the phenomenon of repackaging third-party Android applications.”
• CopperDroid: Automatic Reconstruction of Android Malware Behaviors (2015, methodology): “Recent studies also report how easily mobile marketplaces have been abused to host malware or seemingly legitimate applications embedding malicious components [46].”
• Reliable Third-Party Library Detection in Android and its Security Applications (2016, methodology): “Prior work computed the similarity between apps using code-based similarity techniques [5, 17, 48] or by extracting semantic features from program dependency graphs [6, 7].”
• Structural detection of android malware using embedded call graphs (2013, neutral): “[42] as well as Hanna et al.”
• The Evolution of Android Malware and Android Analysis Techniques (2017, methodology): “As a result of these downfalls, exemplified by the Google App Verification system released in 2012 [Jiang 2012], more effort has been dedicated to implementing semantic signatures, signatures based on functions or methods [Crussell et al. 2012; Zhou et al. 2012].”
• Achieving accuracy and scalability simultaneously in detecting application clones on Android markets (2014, methodology): “DroidMOSS [53] uses a fuzzy hashing technique to detect app clones.”
• ANDRUBIS -- 1,000,000 Apps Later: A View on Current Android Malware Behaviors (2014, background): “Further studies on malware behavior mainly focused on the practice of repackaging and the pervasiveness of repackaged apps in alternative app stores [62,63].”
• Android Security: A Survey of Issues, Malware Penetration, and Defenses (2015, methodology): “Goal: Repackaged App Detection Methodology: Static Deployment: Off-Device DroidMOSS [4] is an app repackaging detection prototype”
• Attack of the Clones: Detecting Cloned Applications on Android Markets (2012, methodology): “Android application cloning has been reported by developers and the academic community [22, 23, 41].”
• Apposcopy: semantics-based detection of Android malware through static analysis (2014, background): “Some recent papers address the detection of re-packaged apps which often inject adware or malicious features into legitimate apps [39, 38, 15, 14].”
• DroidMat: Android Malware Detection through Manifest and API Calls Tracing (2012, background): “[36] consider instructions (opcode) for detecting repackaged smartphone applications in third-party android marketplaces.”