Citation: Luo, J.; Zhang, Y.; Wu, Y.;
Xu, Y.; Guo, X.; Shang, B. A Multi-
Channel Contrastive Learning
Network Based Intrusion Detection
Method. Electronics 2023, 12, 949.
https://doi.org/10.3390/
electronics12040949
Academic Editors: Phivos Mylonas,
Katia Lida Kermanidis and
Manolis Maragoudakis
Received: 11 January 2023
Revised: 4 February 2023
Accepted: 6 February 2023
Published: 14 February 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Article
A Multi-Channel Contrastive Learning Network Based
Intrusion Detection Method
Jian Luo
1,
* , Yiying Zhang
1
, Yannian Wu
2
, Yao Xu
1
, Xiaoyan Guo
3
and Boxiang Shang
4
1
Department of Internet of Things Engineering, Tianjin University of Science and Technology,
Tianjin 300457, China
2
Shenzhen Guodian Technology Communication Co., Shenzhen 518028, China
3
Information and Communication Company, State Grid Tianjin Electric Power Company, Tianjin 300140, China
4
State Grid Tianjin Electric Power Company, Tianjin 300131, China
* Correspondence: steelknife@163.com
Abstract:
Network intrusion data are characterized by high feature dimensionality, extreme category
imbalance, and complex nonlinear relationships between features and categories. The actual detection
accuracy of existing supervised intrusion-detection models performs poorly. To address this problem,
this paper proposes a multi-channel contrastive learning network-based intrusion-detection method
(MCLDM), which combines feature learning in the multi-channel supervised contrastive learning
stage and feature extraction in the multi-channel unsupervised contrastive learning stage to train
an effective intrusion-detection model. The objective is to research whether feature enrichment
and the use of contrastive learning for specific classes of network intrusion data can improve the
accuracy of the model. The model is based on an autoencoder to achieve feature reconstruction
with supervised contrastive learning and for implementing multi-channel data reconstruction. In
the next stage of unsupervised contrastive learning, the extraction of features is implemented using
triplet convolutional neural networks (TCNN) to achieve the classification of intrusion data. Through
experimental analysis, the multichannel contrastive learning network-based intrusion-detection
method achieves 98.43% accuracy in dataset CICIDS17 and 93.94% accuracy in dataset KDDCUP99.
Keywords:
network intrusion detection; feature reconstruction; autoencoder; multi-channel; contrastive
learning
1. Introduction
The network is a national infrastructure and one of the primary targets of attack
in modern warfare, where defense against cyber attacks has become a growing concern
for researchers. In 2020, Brazil’s Light S.A. electricity company was hacked to extort
$14 million
in ransom, and in late February 2022, the Internet was frequently attacked and
controlled from abroad, and cross-space cyber attacks were carried out against Russia and
Ukraine. A network intrusion-detection system is achieved by analyzing the characteristics
of network data streams to determine the network streams as normal data streams and
attack data streams. Intrusion-detection systems are still challenging in the face of the high
dimensionality of data features and extreme imbalance of intrusion categories, and the
systems exhibit low accuracy and high false alarm rates. To solve the above problems,
numerous researchers have mainly focused on machine learning methods [
1
], deep learning
methods [2], and contrastive learning [3].
Various machine learning and deep learning-based solutions have been proposed
in the past decades. Among them, machine learning-based network intrusion network
detection systems rely mainly on feature engineering, so as to learn information about
the characteristics of network intrusion data [
4
]. Deep learning-based network intrusion-
detection approaches, on the other hand, do not rely on huge feature engineering, but
Electronics 2023, 12, 949. https://doi.org/10.3390/electronics12040949 https://www.mdpi.com/journal/electronics
