TIIS (한국인터넷정보학회)
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| 한글제목(Korean Title) |
Android malicious code Classification using Deep Belief Network |
| 영문제목(English Title) |
Android malicious code Classification using Deep Belief Network |
| 저자(Author) |
Luo Shiqi
Tian Shengwei
Yu Long
Yu Jiong
Sun Hua
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| 원문수록처(Citation) |
VOL 12 NO. 01 PP. 0454 ~ 0475 (2018. 01) |
한글내용
(Korean Abstract) |
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영문내용
(English 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.
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| 키워드(Keyword) |
malware classification
texture image
uncompressed gray-scale
Deep Belief Network
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| 파일첨부 |
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