Abstract
Security has become a serious problem for Android system as the number of Android malware increases rapidly. A great amount of effort has been devoted to protect Android devices against the threats of malware. Majority of the existing work use two-class classification methods which suffer the overfitting problem due to the lack of malicious samples. This will result in poor performance of detecting zero-day malware attacks. In this paper, we evaluated the performance of various one-class feature selection and classification methods for zero-day Android malware detection. Unlike two-class methods, one-class methods only use benign samples to build the detection model which overcomes the overfitting problem. Our results demonstrate the capability of the one-class methods over the two-class methods in detecting zero-day Android malware attacks.
This paper is part of Yang Wang’s dissertation which has not been published in other conference or journal.
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Acknowledgements
The work of Jun Zheng was supported in part by the National Science Foundation under EPSCoR Cooperative Agreement OIA-1757207.
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Wang, Y., Zheng, J. (2020). An Evaluation of One-Class Feature Selection and Classification for Zero-Day Android Malware Detection. In: Latifi, S. (eds) 17th International Conference on Information Technology–New Generations (ITNG 2020). Advances in Intelligent Systems and Computing, vol 1134. Springer, Cham. https://doi.org/10.1007/978-3-030-43020-7_15
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DOI: https://doi.org/10.1007/978-3-030-43020-7_15
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