Attack of the Clones: Detecting Cloned Applications on Android Markets

By Jonathan Crussell, Clint Gibler, Hao Chen. European Symposium on Research in Computer Security, 2012

319 citations recorded by the source corpus.

Subfields: Information Systems, Software, Signal Processing

View Paper: https://www.semanticscholar.org/paper/16536516bd111dbb594b128d81dd1da71cfede07

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Citations in the corpus

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].”
*droid (2016, methodology): “The use of native code has historically been limited to a small portion of applications in the market (5% to 7% [Crussell et al. 2012]).”
Achieving accuracy and scalability simultaneously in detecting application clones on Android markets (2014, methodology): “DNADroid [9] detects Android app clones by performing subgraph isomorphism comparison on PDGs.”
Stack Overflow Considered Harmful? The Impact of Copy&Paste on Android Application Security (2017, methodology): “Following this approach provides robustness to high-level modifications and trivial control-flow alterations, as well as non-malicious code insertions/deletion, code reordering, constants modifications and method restructurings as described in [35].”
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].”
A measurement study of google play (2014, methodology): “These approaches are computationally complex as they are based on application code analysis, either using pairwise comparisons [16, 13, 27, 42] or comparing applications to a subset of their closest neighbors [14, 41].”
DeepSim: deep learning code functional similarity (2018, background): “There exist a few techniques [15, 17, 32, 37] that construct a program dependence graph (PDG) for each code fragment and measure code similarity through finding isomorphic sub-graphs.”
The Evolution of Android Malware and Android Analysis Techniques (2017, methodology): “Most malware detection methods are anomaly based [Shabtai et al. 2012] (e.g., deﬁning normal and abnormal attribute sets), misuse based [Yajin Zhou 2013] (e.g., identifying speciﬁc malicious actions), or signature based (e.g. semantic or bytecode) [Crussell et al. 2012].”
Structural detection of android malware using embedded call graphs (2013, methodology): “[7] present a method called DNADroid, which compares program dependence graphs.”
PUMA: programmable UI-automation for large-scale dynamic analysis of mobile apps (2014, methodology): “Static analysis of app binaries, as used in prior work to identify privacy [21] and security [10, 12] problems, or app clones [9] etc. , can scale to a large number of apps.”
Learning-Based Recursive Aggregation of Abstract Syntax Trees for Code Clone Detection (2019, background): “This problem is likely to become even more relevant in the future, due to today’s ease of online collaboration and code distribution via open-source code repositories [3], question-answering forums [4], and app stores [5].”
Static analysis of android apps: A systematic literature review (2017, methodology): “WALA IR A3E [57], AAPL [61], AnDarwin [49], AndroidLeaks [80], AsDroid [95], Asynchronizer [144], CHEX [8], DNADroid [48], Hopper [79], ORBIT [154], Poeplau et al.”
A Survey of App Store Analysis for Software Engineering (2017, methodology): “[50], 2012 g ESORICS 75,000 Peng et al.”