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Malware Collusion Attack Against Machine Learning Based Methods: Issues and Countermeasures

  • Hongyi Chen
  • Jinshu SuEmail author
  • Linbo Qiao
  • Yi Zhang
  • Qin Xin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11067)

Abstract

Android has become the most popular platform for mobile devices, and also it has become a popular target for malware developers. At the same time, researchers have proposed a large number of methods, both static and dynamic analysis methods, to fight against malwares. Among these, Machine learning based methods are quite effective in Android malware detection, the accuracy of which can be up to 98%. Thus, malware developers have the incentives to develop more advanced malwares to evade detection. This paper presents an adversary attack pattern that will compromise current machine learning based malware detection methods. The malware developers can perform this attack easily by splitting malicious payload into two or more apps. The split apps will all be classified as benign by current methods. Thus, we proposed a method to deal with this issue. This approach, realized in a tool, called ColluDroid, can identify the collusion apps by analyzing the communication between apps. The evaluation results show that ColluDroid is effective in finding out the collusion apps. Also, we showed that it’s easy to split an app to evade detection. According to our split simulation, the evasion rate is 78%, when split into two apps; while the evasion rate comes to 94.8%, when split into three apps.

Keywords

Android security Machine learning Collusion attack 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Hongyi Chen
    • 1
  • Jinshu Su
    • 1
    Email author
  • Linbo Qiao
    • 1
  • Yi Zhang
    • 1
  • Qin Xin
    • 2
  1. 1.National Key Laboratory for Parallel and Distributed ProcessingNational University of Defense TechnologyChangshaChina
  2. 2.Faculty of Science and TechnologyUniversity of the Faroe IslandsTorshavnFaroe Islands

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