Juggler-ResNet: A Flexible and High-Speed ResNet Optimization Method for Intrusion Detection System in Software-Defined Industrial Networks

Zongwei ZHU*, Wenjie ZHAI, Huanghe LIU, Jiawei GENG, Mingliang ZHOU*, Cheng JI, Gangyong JIA

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

24 Citations (Scopus)

Abstract

ResNets are widely used in the intrusion detection system (IDS) of software-defined industrial network to construct accurate intelligence detection of network attacks. However, the IDS based on ResNets has a long detecting interval because of the fine-grained operator and intermediate outcomes of the multi-branch architecture of ResNets. To address this problem, in this article, we propose Juggler-ResNet with a fusible residual structure that preserves the feature extraction ability of the residual structure and enables equivalent transformation to linear topology to support low latency inference service in the industrial application (e.g., malicious network behavior detection, fault diagnosis, etc.). First, we propose a fusible multibranch residual structure to avoid gradient vanishing problems in the training phase. Second, we convert it to linear-topology by using a set of equivalent fusion operators. Finally, the linear-topology model is deployed to accelerate inference speed. Our experimental results on CIFAR-10 and CIFAR-100 show that fusible residual structure can achieve 2.08-4.3x acceleration with state-of-the-art level accuracy performance.

Original languageEnglish
Pages (from-to)4224-4233
Number of pages10
JournalIEEE Transactions on Industrial Informatics
Volume18
Issue number6
Early online date21 Oct 2021
DOIs
Publication statusPublished - Jun 2022
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2005-2012 IEEE.

Keywords

  • Image classification
  • inference acceleration
  • intrusion detection system (IDN)
  • optimization method
  • ResNets
  • software-defined industrial networks (SDINs)

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