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A Modified FlowDroid Based on Chi-Square Test of Permissions.

Hongzhaoning Kang1, Gang Liu1, Zhengping Wu1

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This study improves Android app security by developing a new static taint analysis method. It reduces false alarms and enhances detection efficiency for malicious data flows using feature permissions and risk ratings.

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Area of Science:

  • Computer Science
  • Software Engineering
  • Cybersecurity

Background:

  • Android applications (apps) are prevalent in various fields, including IoT and embedded systems.
  • Malicious apps exploit app permissions to compromise user privacy and data security.
  • Existing static taint analysis tools like FlowDroid face challenges with complex detection and false alarms.

Purpose of the Study:

  • To propose an improved static detection method for Android applications.
  • To address the complexity and false alarm issues in existing taint analysis tools.
  • To enhance the identification of dangerous data flows in Android apps.

Main Methods:

  • Utilized Chi-square test to identify correlated permissions associated with malicious apps.
  • Employed mutual information for clustering permissions into feature permission clusters.
  • Developed a risk calculation method based on individual and combined permissions to detect hazardous data flows.

Main Results:

  • The proposed method significantly enhances the efficiency of detecting dangerous data flows.
  • Maintained high accuracy in identifying malicious data flows.
  • Demonstrated a reduction in complexity and false alarms compared to FlowDroid.

Conclusions:

  • The improved static detection method offers a more efficient and accurate approach to Android app security.
  • Feature permission clustering and risk rating effectively identify dangerous data flows.
  • This research contributes to more robust security analysis for Android applications.