A Packet Sequence Permutation-Aware Approach to Robust Network Traffic Classification

Yanzhuo Jiang, Xueman Wang, Yingxu Lai, Yipeng Wang

Abstract: Anomalies in packet length sequences caused by network topology structure and congestion greatly impact the performance of early network traffic classification. Additionally, insufficient differentiation of packet length sequences using a small number of packets also affects the performance. In this letter, we propose SePeric, a packet sequence permutation-aware approach to robust network traffic classification. By exploring the correlations within packet length sequences and adjusting them to eliminate the effects of anomalous sequence orders, as well as extracting additional features from the byte sequence of the first packet to supplement the insufficient differentiation in packet length sequences.