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As the emerging services have increasingly strict requirements on quality ofservice (QoS), such as millisecond network service latency ect., networktraffic classification technology is required to assist more advanced networkmanagement and monitoring capabilities. So far as we know, the delays offlow-granularity classification methods are difficult to meet the real-timerequirements for too long packet-waiting time, whereas the presentpacket-granularity classification methods may have problems related to privacyprotection due to using excessive user payloads. To solve the above problems,we proposed a network traffic classification method only by the IP packetheader, which satisfies the requirements of both user's privacy protection andclassification performances. We opted to remove the IP address from the headerinformation of the network layer and utilized the remaining 12-byte IP packetheader information as input for the model. Additionally, we examined thevariations in header value distributions among different categories of networktraffic samples. And, the external attention is also introduced to form theonline classification framework, which performs well for its low timecomplexity and strong ability to enhance high-dimensional classificationfeatures. The experiments on three open-source datasets show that our averageaccuracy can reach upon 94.57%, and the classification time is shortened tomeet the real-time requirements (0.35ms for a single packet).

Network Traffic Classification Based on External Attention by IP Packet Header