Detecting Privacy Breaches in Mobile Network Traffic with Extreme Gradient Boosting and Statistical Flow Features

With the rapid growth of mobile application usage on smartphones, personal information is being increasingly collected and processed, often without sufficient user consent or awareness. The vast amounts of personal data – ranging from location information to contacts and messages – are vulnerable to unauthorized access, creating significant privacy concerns. As a result, detecting privacy breaches in mobile traffic is essential for protecting user data and ensuring that applications adhere to privacy standards. While existing machine learning techniques for traffic monitoring have made strides in identifying privacy leaks, many still struggle with detecting obfuscated or undefined data types, which are often used to bypass traditional detection methods. This paper proposes a novel approach to detecting privacy leaks in mobile network traffic by leveraging statistical features such as flow patterns, packet sizes, and transmission frequencies. The model employs Extreme Gradient Boosting (EGB) to classify traffic patterns and identify instances of privacy violations. EGB, known for its high accuracy and scalability, is trained to detect various traffic patterns, including those associated with obfuscated or encrypted data. Despite the inherent variability in statistical features due to individual user behaviors, the proposed method proves highly effective in identifying privacy leaks. Extensive simulation results validate the performance of the approach, demonstrating its ability to scale across different datasets while maintaining high accuracy. This method provides a robust and scalable solution for detecting privacy breaches, ensuring the protection of personal data and enhancing user privacy on mobile platforms.HIGHLIGHTSThis work focuses on detecting privacy breaches with the utilization of statistical features such as transmission frequency, flow patterns including global and local patterns, and packet size.Extreme Gradient Boosting (EGB)-based traffic monitoring method is used.The detector used for detection is trained using EGB to detect different traffic patterns.The proposed work detects obfuscated and undefined privacy leakages.Simulation is effectuated to analyze various performances of the proposed work.