Flow-MAE: Leveraging Masked AutoEncoder for Accurate, Efficient and Robust Malicious Traffic Classification

Authors: Zijun Hang, Yuliang Lu, Yongjie Wang, Yi Xie - Proceedings of the 26th International Symposium on Research in Attacks, Intrusions and Defenses 2023 cited by 41

Malicious traffic classification is crucial for Intrusion Detection Systems (IDS). However, traditional Machine Learning approaches necessitate expert knowledge and a significant amount of well-labeled data. Although recent studies have employed pre-training models from the Natural Language Processing domain, such as ET-BERT, for traffic classification, their effectiveness is impeded by limited input length and fixed Byte Pair Encoding.