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

Android malicious code Classification using Deep Belief Network

Vol. 12, No.1, January 31, 2018
10.3837/tiis.2018.01.022, Download Paper (Free):

Abstract

This paper presents a novel Android malware classification model planned to classify and categorize Android malicious code at Drebin dataset. The amount of malicious mobile application targeting Android based smartphones has increased rapidly. In this paper, Restricted Boltzmann Machine and Deep Belief Network are used to classify malware into families of Android application. A texture-fingerprint based approach is proposed to extract or detect the feature of malware content. A malware has a unique "image texture" in feature spatial relations. The method uses information on texture image extracted from malicious or benign code, which are mapped to uncompressed gray-scale according to the texture image-based approach. By studying and extracting the implicit features of the API call from a large number of training samples, we get the original dynamic activity features sets. In order to improve the accuracy of classification algorithm on the features selection, on the basis of which, it combines the implicit features of the texture image and API call in malicious code, to train Restricted Boltzmann Machine and Back Propagation. In an evaluation with different malware and benign samples, the experimental results suggest that the usability of this method---using Deep Belief Network to classify Android malware by their texture images and API calls, it detects more than 94% of the malware with few false alarms. Which is higher than shallow machine learning algorithm clearly.


Statistics

Show / Hide Statistics

Statistics (Cumulative Counts from December 1st, 2015)
Multiple requests among the same browser session are counted as one view.
If you mouse over a chart, the values of data points will be shown.


Cite this article

[IEEE Style]
Luo Shiqi, Tian Shengwei, Yu Long, Yu Jiong and Sun Hua, "Android malicious code Classification using Deep Belief Network," KSII Transactions on Internet and Information Systems, vol. 12, no. 1, pp. 454-475, 2018. DOI: 10.3837/tiis.2018.01.022

[ACM Style]
Shiqi, L., Shengwei, T., Long, Y., Jiong, Y., and Hua, S. 2018. Android malicious code Classification using Deep Belief Network. KSII Transactions on Internet and Information Systems, 12, 1, (2018), 454-475. DOI: 10.3837/tiis.2018.01.022