期刊
IEEE ACCESS
卷 8, 期 -, 页码 182065-182077出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2020.3029190
关键词
Cryptography; Neural networks; Feature extraction; Protocols; Machine learning; Payloads; Androids; Mobile app identification; encrypted traffic classification; neural network; deep learning
资金
- National Key Research and Development Program of China [2018YFB0204301]
The proliferation of handheld devices has led to an explosive growth of mobile traffic volumes on the Internet. Identifying mobile apps from network traffic has become a crucial task for mobile network management and security. Traditionally, the design of accurate identifiers relies on the deep packet inspection (DPI) techniques. However, such approaches have become less effective with the raising adoption of encrypted protocols in mobile applications (mostly TLS). To address the problem, various machine learning methods have been studied and used. Most of them use linear classifiers on top of hand-engineered features, which are unreliable due to the complexity of mobile traffic. In this article we propose App-Net, an end-to-end hybrid neural network for mobile app identification from encrypted TLS traffic. App-Net is designed by combining RNN and CNN in a parallel way and can automatically learn effective features from raw TLS flows. With coordinated fusion and optimized training, the hybrid and multimodal architecture is able to characterize both flow sequence patterns and app signatures to learn a joint flow-app embedding. We evaluate App-Net on a real-world dataset covering 80 apps. The results show that our method can achieve an excellent performance and outperform the state-of-the-art methods.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据