Detecting Anomalies in Communication Packet Streams Based on Generative Adversarial Networks

Di Zhang*, Qiang Niu, Xingbao Qiu

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

3 Citations (Scopus)


The fault diagnosis in a modern communication system is traditionally supposed to be difficult, or even impractical for a purely data-driven machine learning approach, for it is a humanmade system of intensive knowledge. A few labeled raw packet streams extracted from fault archive can hardly be sufficient to deduce the intricate logic of underlying protocols. In this paper, we supplement these limited samples with two inexhaustible data sources: the unlabeled records probed from a system in service, and the labeled data simulated in an emulation environment. To transfer their inherent knowledge to the target domain, we construct a directed information flow graph, whose nodes are neural network components consisting of two generators, three discriminators and one classifier, and whose every forward path represents a pair of adversarial optimization goals, in accord with the semi-supervised and transfer learning demands. The multi-headed network can be trained in an alternative approach, at each iteration of which we select one target to update the weights along the path upstream, and refresh the residual layer-wisely to all outputs downstream. The actual results show that it can achieve comparable accuracy on classifying Transmission Control Protocol (TCP) streams without deliberate expert features. The solution has relieved operation engineers from massive works of understanding and maintaining rules, and provided a quick solution independent of specific protocols.

Original languageEnglish
Title of host publicationWireless Algorithms, Systems, and Applications - 14th International Conference, WASA 2019, Proceedings
EditorsEdoardo S. Biagioni, Yao Zheng, Siyao Cheng
PublisherSpringer Verlag
Number of pages12
ISBN (Print)9783030235963
Publication statusPublished - 2019
Event14th International Conference on Wireless Algorithms, Systems, and Applications, WASA 2019 - Honolulu, United States
Duration: 24 Jun 201926 Jun 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11604 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference14th International Conference on Wireless Algorithms, Systems, and Applications, WASA 2019
Country/TerritoryUnited States

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