A Glimpse of the Whole: Path Optimization Prototypical Network for Few-Shot Encrypted Traffic Classification

With the prosperous application of encryption technology in network traffic, monitoring and analyzing network traffic efficiently become more and more challenging. Existing traffic classification methods mostly rely on sufficient and balanced training data, which inevitably require overwhelming labeling effort. Therefore, it is necessary to investigate an effective solution to relieve the enormous burden on annotating network data. To address the above issues, in this paper, we model the encrypted traffic classification as few-shot learning based on metric-learning and propose Path Optimization Prototypical Network (POPNet). Firstly, POPNet utilizes embedding model to map network traffic into a high dimensional metric space. Secondly, the distance between the embedded samples is optimized to aggregate samples with same category while estranging the distinct ones. Moreover, path optimization strategies are carefully designed to compress the searching space to obtain an efficient solution. Experimental results on regenerated datasets of real-world network traffic have demonstrated the effectiveness of our proposed POPNet. It is encouraging to see that, with barely a few training samples, POPNet has achieved superior performance on encrypted network traffic classification among the state-of-thearts, and its performance is immune to the deduction of training samples. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. Conference’17, July 2017, Washington, DC, USA © 2020 Association for Computing Machinery. ACM ISBN 978-x-xxxx-xxxx-x/YY/MM. . . $15.00 https://doi.org/10.1145/nnnnnnn.nnnnnnn CCS CONCEPTS •Networks→Networkmanagement; • Information systems → Traffic analysis; • Computing methodologies→Model verification and validation.