Online Network Traffic Classification Based on External Attention and Convolution by IP Packet Header
CoRR(2023)
摘要
Network traffic classification is an important part of network monitoring and
network management. Three traditional methods for network traffic
classification are flow-based, session-based, and packet-based, while
flow-based and session-based methods cannot meet the real-time requirements and
existing packet-based methods will violate user's privacy. To solve the above
problems, we propose a network traffic classification method only by the IP
packet header, which satisfies the requirements of both the user's privacy
protection and online classification performances. Through statistical
analyses, we find that IP packet header information is effective on the network
traffic classification tasks and this conclusion is also demonstrated by
experiments. Furthermore, we propose a novel external attention and convolution
mixed (ECM) model for online network traffic classification. This model adopts
both low-computational complexity external attention and convolution to
respectively extract the byte-level and packet-level characteristics for
traffic classification. Therefore, it can achieve high classification accuracy
and low time consumption. The experiments show that ECM can achieve the highest
classification accuracy and the lowest delay, compared with other state-of-art
models. The accuracy can respectively achieve 98.39
and the classification time is shorten to meet the real-time requirements.
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