DroidVecDeep: Android Malware Detection Based on Word2Vec and Deep Belief Network

• Tieming Chen,
• Qingyu Mao,
• Mingqi Lv,
• Hongbing Cheng,
• Yinglong Li,

Vol. 13, No. 4, April 29, 2019
10.3837/tiis.2019.04.025, Download Paper (Free):

• android security
• Malware detection
• Deep Learning
• distributed representation
• Word2vec

Abstract

With the proliferation of the Android malicious applications, malware becomes more capable of hiding or confusing its malicious intent through the use of code obfuscation, which has significantly weaken the effectiveness of the conventional defense mechanisms. Therefore, in order to effectively detect unknown malicious applications on the Android platform, we propose DroidVecDeep, an Android malware detection method using deep learning technique. First, we extract various features and rank them using Mean Decrease Impurity. Second, we transform the features into compact vectors based on word2vec. Finally, we train the classifier based on deep learning model. A comprehensive experimental study on a real sample collection was performed to compare various malware detection approaches. Experimental results demonstrate that the proposed method outperforms other Android malware detection techniques.

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