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DroidDelver: An Android Malware Detection System Using Deep Belief Network Based on API Call Blocks

  • Shifu Hou
  • Aaron Saas
  • Yanfang YeEmail author
  • Lifei Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9998)

Abstract

Because of the explosive growth of Android malware and due to the severity of its damages, the detection of Android malware has become an increasing important topic in cyber security. Currently, the major defense against Android malware is commercial mobile security products which mainly use signature-based method for detection. However, attackers can easily devise methods, such as obfuscation and repackaging, to evade the detection, which calls for new defensive techniques that are harder to evade. In this paper, resting on the analysis of Application Programming Interface (API) calls extracted from the smali files, we further categorize the API calls which belong to the some method in the smali code into a block. Based on the generated code blocks, we then apply a deep learning framework (i.e., Deep Belief Network) for newly unknown Android malware detection. Using a real sample collection from Comodo Cloud Security Center, a comprehensive experimental study is performed to compare various malware detection approaches. Promising experimental results demonstrate that DroidDelver which integrates our proposed method outperform other alternative Android malware detection techniques.

Keywords

Android malware detection API call block Deep belief network 

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Department of Computer Science and Electrical EngineeringWest Virginia UniversityMorgantownUSA
  2. 2.School of Mathematics and Computer ScienceFujian Normal UniversityFuzhouChina

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