An Automatic Approach of Building Threat Patterns in Android

  • Chia-Mei Chen
  • Yu-Hsuan Tsai
  • Gu-Hsin Lai
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 733)


Nowadays, handheld devices have become popular but volume of malwares on mobile platform has also grown rapidly. To detect mobile malware, static approaches and dynamic approaches are two common ways used to analyze suspicious applications. Dynamic approaches detect malware base on the actual behaviors of applications, but how to trigger malicious behavior and the efficient of dynamic approaches are the difficulties of this kind of approaches. Due to the limited resource of mobile devices, static analysis approach is the practicable way to detect malwares on mobile device. Anti-virus software is the typical paradigm of static analysis approach. However, the effectiveness of Anti-virus software rely on its signatures. How to find an efficient and automatic way to build thread pattern of mobile malware is a critical issue to detect new or zero-day malware.

In this paper, a detect mechanism based on data flow is proposed. The proposed system analyzes the function calls and the data flow to identify malicious behaviors in Android mobile devices. Machine learning approach is used to build threat patterns automatically within a great volume of applications. The experimental result shows that the proposed system could detect malware with high accuracy and low false positive rate.


Android malware Data flow Machine learning 


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.National Sun Yat-Sen UniversityKaohsiungTaiwan
  2. 2.Taiwan Police CollegeTaipeiTaiwan

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