• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Deep Learning in Drebin: Android malware Image Texture Median Filter Analysis and Detection

Vol. 13, No. 7, July 30, 2019
10.3837/tiis.2019.07.018, Download Paper (Free):

Abstract

This paper proposes an Image Texture Median Filter (ITMF) to analyze and detect Android malware on Drebin datasets1. We design a model of “ITMF” combined with Image Processing of Median Filter (MF) to reflect the similarity of the malware binary file block. At the same time, using the MAEVS (Malware Activity Embedding in Vector Space) to reflect the potential dynamic activity of malware. In order to ensure the improvement of the classification accuracy, the above-mentioned features(ITMF feature and MAEVS feature)are studied to train Restricted Boltzmann Machine (RBM) and Back Propagation (BP). The experimental results show that the model has an average accuracy rate of 95.43% with few false alarms. to Android malicious code, which is significantly higher than 95.2% of without ITMF, 93.8% of shallow machine learning model SVM, 94.8% of KNN, 94.6% of ANN.


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Cite this article

[IEEE Style]
L. Shi-qi, N. Bo, J. Ping, T. Sheng-wei, Y. Long, W. Rui-jin, "Deep Learning in Drebin: Android malware Image Texture Median Filter Analysis and Detection," KSII Transactions on Internet and Information Systems, vol. 13, no. 7, pp. 3654-3670, 2019. DOI: 10.3837/tiis.2019.07.018.

[ACM Style]
Luo Shi-qi, Ni Bo, Jiang Ping, Tian Sheng-wei, Yu Long, and Wang Rui-jin. 2019. Deep Learning in Drebin: Android malware Image Texture Median Filter Analysis and Detection. KSII Transactions on Internet and Information Systems, 13, 7, (2019), 3654-3670. DOI: 10.3837/tiis.2019.07.018.

[BibTeX Style]
@article{tiis:22162, title="Deep Learning in Drebin: Android malware Image Texture Median Filter Analysis and Detection", author="Luo Shi-qi and Ni Bo and Jiang Ping and Tian Sheng-wei and Yu Long and Wang Rui-jin and ", journal="KSII Transactions on Internet and Information Systems", DOI={10.3837/tiis.2019.07.018}, volume={13}, number={7}, year="2019", month={July}, pages={3654-3670}}