Android Malware Detection Based on Network Traffic Using Decision Tree Algorithm

  • Aqil Zulkifli
  • Isredza Rahmi A. Hamid
  • Wahidah Md Shah
  • Zubaile Abdullah
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 700)

Abstract

Android mobile operating system has well developed and gained absolute popularity among user. Although android is an open source operating system, it fits user daily life requirement nowadays. However, this is the reason why android malware keep on increasing every year. There are various method used to detect the occurrence of android malware such as based on static or dynamic analysis. Static analysis is favourable approach because it is quick and inexpensive. However, the static analysis unable to monitor the malicious application behavior during runtime. Therefore, we proposed a dynamic detection technique based on network traffic which records the application behavior during runtime. We consider seven network traffic features extracted from Drebin and Contagiodumpset dataset. The Drebin dataset achieved higher accuracy value with 98.4% as compared to Contagiodumpset dataset when tested using J48 decision tree algorithm.

Keywords

Android Malware Decision tree algorithm 

Notes

Acknowledgements

The authors express appreciation to the Universiti Tun Hussein Onn Malaysia (UTHM). This research is supported by Short Term Grant vot number U653 and Gates IT Solution Sdn. Bhd. under its publication scheme.

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Aqil Zulkifli
    • 1
  • Isredza Rahmi A. Hamid
    • 1
  • Wahidah Md Shah
    • 2
  • Zubaile Abdullah
    • 1
  1. 1.Information Security Interest Group (ISIG), Faculty of Computer Science and Information TechnologyUniversiti Tun Hussein OnnParit RajaMalaysia
  2. 2.Faculty of Information Technology and CommunicationUniversiti Teknikal Malaysia MelakaDurian TunggalMalaysia

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