Cerberus: Efficient OSPS Traffic Identification through Multi-Task Learning

The privacy protection capabilities of open source proxy software (OSPS) while browsing the Internet have sparked great interest from both industry and academia, bringing forth pressing security concerns. Currently, using artificial intelligence for traffic identification is the most promising direction. Due to the wide variety and rich configuration of OSPS, it is not feasible to train models for all tasks and deploy them on the same network device. It is a novel idea to improve efficiency by leveraging multi-task learning. However, the related studies still have three shortcomings. First, improving the performance of the main task through auxiliary tasks does not apply to equally important OSPS identification tasks. Second, the model’s ability to characterize traffic is weak, resulting in performance gaps between different tasks. Finally, the influence of task difficulty on convergence speed is ignored, which is easy to cause overfitting and underfitting. Aiming at the shortcomings, we propose Cerberus, an OSPS traffic identification scheme based on multi-task learning. The main contributions of our work can be summarized in three aspects. Firstly, a high-quality dataset is constructed through traffic collection, and three OSPS traffic identification tasks are defined on it. Secondly, an identification model is designed by optimizing the ability to characterize traffic and balancing the convergence speed of multiple tasks. Finally, Cerberus is verified through comparative experiments. Its classification performance is better than both single-task and multi-task solutions. Besides, Cerberus runs fast and consumes few resources, making it suitable for deployment on network devices.