Machine learning aided Android malware classification
By Nikola Milosevic, Ali Dehghantanha, Kim-Kwang Raymond Choo. Computers & electrical engineering, 2017
328 citations recorded by the source corpus.
The widespread adoption of Android devices and their capability to access significant private and confidential information have resulted in these devices being targeted by malware developers. Existing Android malware analysis techniques can be broadly categorized into static and dynamic analysis. In this paper, we present two machine learning aided approaches for static analysis of Android malware. The first approach is based on permissions and the other is based on source code analysis utilizing a bag-of-words representation model. Our permission-based model is computationally inexpensive, and is implemented as the feature of OWASP Seraphimdroid Android app that can be obtained from Google Play Store. Our evaluations of both approaches indicate an F-score of 95.1% and F-measure of 89% for the source code-based classification and permission-based classification models, respectively.
Subfields: Information Systems, Software, Signal Processing
View Paper: https://www.semanticscholar.org/paper/fc4a8062d5b8dfd34e61f46ebedade56e01ef001
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Citations in the corpus
- Deep Reinforcement Learning for Cyber Security (2019, methodology): “Among these ML applications, unsupervised and supervised learning methods have been used widely for intrusion detection [10]–[15], malware detection [16]–[19], cyber-physical attacks [20], [21], and data privacy protection [22].”