Statistical Feature-based Personal Information Detection in Mobile Network Traffic

TL;DR

This paper introduces a novel traffic monitoring-based method using statistical features and machine learning to detect various types of personal information leaks in mobile app traffic, including obfuscated leaks.

Contribution

It proposes the first approach utilizing statistical features for personal information detection, improving detection of obfuscated and undefined privacy leaks.

Findings

Achieves better detection performance than existing methods.

Capable of identifying obfuscated personal information leaks.

Detects various types of privacy leaks using statistical features.

Abstract

With the popularity of smartphones, mobile applications (apps) have penetrated the daily life of people. Although apps provide rich functionalities, they also access a large amount of personal information simultaneously. As a result, privacy concerns are raised. To understand what personal information the apps collect, many solutions are presented to detect privacy leaks in apps. Recently, the traffic monitoring-based privacy leak detection method has shown promising performance and strong scalability. However, it still has some shortcomings. Firstly, it suffers from detecting the leakage of personal information with obfuscation. Secondly, it cannot discover the privacy leaks of undefined type. Aiming at solving the above problems, a new personal information detection method based on traffic monitoring is proposed in this paper. In this paper, statistical features of personal information are designed to depict the occurrence patterns of personal information in the traffic, including local patterns and global patterns. Then a detector is trained based on machine learning algorithms to discover potential personal information with similar patterns. Since the statistical features are independent of the value and type of personal information, the trained detector is capable of identifying various types of privacy leaks and obfuscated privacy leaks. As far as we know, this is the first work that detects personal information based on statistical features. Finally, the experimental results show that the proposed method could achieve better performance than the state-of-the-art.