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Topic Model Based Android Malware Detection

  • Yucai Song
  • Yang Chen
  • Bo LangEmail author
  • Hongyu Liu
  • Shaojie Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11611)

Abstract

Nowadays, the security risks brought by Android malwares are increasing. Machine learning is considered as a potential solution for promoting the performance of malware detection. For machine learning based Android malware detection, feature extraction plays a key role. Thinking the source codes of applications are comparable with text documents, we propose a new Android malware detection method based on the topic model which is an effective technique in text feature extraction. Our method regards the decompiled codes of an application as a text document, and the topic model is used to mine the potential topics in the codes which can reflect the semantic feature of the application. The experimental results demonstrate that, our approach performs better than the state-of-the-art methods. Also, our method mines the features in the application files automatically without manually design, and therefore overcomes the limitation in present methods which relies on experts’ prior knowledge.

Keywords

Android malware detection Topic model Machine learning 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Yucai Song
    • 1
  • Yang Chen
    • 2
  • Bo Lang
    • 1
    Email author
  • Hongyu Liu
    • 1
  • Shaojie Chen
    • 1
  1. 1.State Key Lab of Software Development Environment, School of Computer Science and EngineeringBeihang UniversityBeijingChina
  2. 2.National Computer Network Emergency Response Technical Team/Coordination Center of ChinaBeijingChina

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