Flow-MAE: Leveraging Masked AutoEncoder for Accurate, Efficient and Robust Malicious Traffic Classification
Zijun Hang, Yuliang Lu, Yongjie Wang, Yi Xie
Abstract
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.
Topics & Concepts
Computer scienceAutoencoderIntrusion detection systemByteArtificial intelligenceMachine learningTraffic classificationDomain (mathematical analysis)Encoding (memory)Data miningDeep learningComputer securityOperating systemNetwork packetMathematical analysisMathematicsNetwork Security and Intrusion DetectionInternet Traffic Analysis and Secure E-votingAdvanced Malware Detection Techniques