Fast and Accurate Multi-Task Learning for Encrypted Network Traffic Classification
Source abstract
Abstract
The classification of encrypted traffic plays a crucial role in network management and security. As encrypted network traffic becomes increasingly complicated and challenging to analyze, there is a growing need for more efficient and comprehensive analytical approaches. Our proposed method introduces a novel approach to network traffic classification, utilizing multi-task learning to simultaneously train multiple tasks within a single model. To validate the proposed method, we conducted experiments using the ISCX 2016 VPN/Non-VPN dataset, consisting of three tasks. The proposed method outperformed the majority of existing methods in classification with 99.29%, 97.38%, and 96.89% accuracy in three tasks (i.e., encapsulation, category, and application classification, respectively). The efficiency of the proposed method also demonstrated outstanding performance when compared to methods excluding lightweight models. The proposed approach demonstrates accurate and efficient multi-task classification on encrypted traffic.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Internet Traffic Analysis and Secure E-voting · Network Security and Intrusion Detection · Advanced Malware Detection Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Computer and Information Science, Korea University, Sejong 30019, Republic of Korea
C4ISR System Development Quality Research Team, Defense Agency for Technology and Quality, Daejeon 35409, Republic of Korea
Funding
Institute of Information & communications Technology Planning & Evaluation (IITP)
References (54)
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