Abstract
Android platforms are known as the less security smartphone devices. The increasing number of malicious apps published on Android markets suppose an important threat to users sensitive data, compromising more devices everyday. The commercial solutions that aims to fight against this malware are based on signature methodologies whose detection ratio is low. Furthermore, these engines can be easily defeated by obfuscation techniques, which are extremely common in app plagiarism. This work aims to improve malware detection using only the binary information and the permissions that are normally used by the anti-virus engines, in order to provide a scalable solution based on machine learning. In order to evaluate the performance of this approach, we carry out our experiments using 5000 malware and 5000 benign-ware, and compare the results with 56 Anti-Virus Engines from VirusTotal.
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Martín, A., Menéndez, H.D., Camacho, D. (2017). String-based Malware Detection for Android Environments. In: Badica, C., et al. Intelligent Distributed Computing X. IDC 2016. Studies in Computational Intelligence, vol 678. Springer, Cham. https://doi.org/10.1007/978-3-319-48829-5_10
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DOI: https://doi.org/10.1007/978-3-319-48829-5_10
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