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http://dx.doi.org/10.9708/jksci.2021.26.10.019

CNN-based Android Malware Detection Using Reduced Feature Set  

Kim, Dong-Min (Dept. of Computer Science and Engineering, Korea National Defense University)
Lee, Soo-jin (Dept. of Computer Science and Engineering, Korea National Defense University)
Publication Information
Journal of the Korea Society of Computer and Information / v.26, no.10, 2021 , pp. 19-26 More about this Journal
Abstract
The performance of deep learning-based malware detection and classification models depends largely on how to construct a feature set to be applied to training. In this paper, we propose an approach to select the optimal feature set to maximize detection performance for CNN-based Android malware detection. The features to be included in the feature set were selected through the Chi-Square test algorithm, which is widely used for feature selection in machine learning and deep learning. To validate the proposed approach, the CNN model was trained using 36 characteristics selected for the CICANDMAL2017 dataset and then the malware detection performance was measured. As a result, 99.99% of Accuracy was achieved in binary classification and 98.55% in multiclass classification.
Keywords
CNN; Android; Malware; Feature selection; Binary classification; Multiclass classification;
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