一种基于自编码器降维的神经卷积网络入侵检测模型

孙敬; 丁嘉伟; 冯光辉

doi:10.11959/j.issn.1000-0801.2025002

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一种基于自编码器降维的神经卷积网络入侵检测模型

研究与开发 | 更新时间：2025-03-11

- 一种基于自编码器降维的神经卷积网络入侵检测模型
- A neural convolutional network intrusion detection model based on autoencoder dimension reduction
- 电信科学 2025年41卷第2期页码：129-138
- 作者机构：
  
  1.郑州工业应用技术学院信息工程学院，河南郑州 451150
  2.广州大学计算机科学与网络工程学院，广东广州 510006
- 作者简介：
  
  [ "孙敬（1991- ），女，郑州工业应用技术学院信息工程学院讲师，主要研究方向为计算机应用、人工智能。" ]
  [ "丁嘉伟（1986- ），男，郑州工业应用技术学院信息工程学院副教授，主要研究方向为人工智能、管理信息系统。" ]
  [ "冯光辉（1982- ），男，广州大学计算机科学与网络工程学院博士生，主要研究方向为差分隐私、联邦学习等。" ]
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2025002
  中图分类号： TP393
- 纸质出版：2025-02-20
- 稿件说明：
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孙敬,丁嘉伟,冯光辉.一种基于自编码器降维的神经卷积网络入侵检测模型[J].电信科学,2025,41(02):129-138.

SUN Jing,DING Jiawei,FENG Guanghui.A neural convolutional network intrusion detection model based on autoencoder dimension reduction[J].Telecommunications Science,2025,41(02):129-138.
孙敬,丁嘉伟,冯光辉.一种基于自编码器降维的神经卷积网络入侵检测模型[J].电信科学,2025,41(02):129-138. DOI： 10.11959/j.issn.1000-0801.2025002.

SUN Jing,DING Jiawei,FENG Guanghui.A neural convolutional network intrusion detection model based on autoencoder dimension reduction[J].Telecommunications Science,2025,41(02):129-138. DOI： 10.11959/j.issn.1000-0801.2025002.

摘要

为了提升入侵检测的准确率，鉴于自编码器在学习特征方面的优势以及残差网络在构建深层模型方面的成熟应用，提出一种基于特征降维的改进残差网络入侵检测模型（improved residual network intrusion detection model based on feature dimensionality reduction，IRFD），进而缓解传统机器学习入侵检测模型的低准确率问题。IRFD采用堆叠降噪稀疏自编码器策略对数据进行降维，从而提取有效特征。利用卷积注意力机制对残差网络进行改进，构建能提取关键特征的分类网络，并利用两个典型的入侵检测数据集验证IRFD的检测性能。实验结果表明，IRFD在数据集UNSW-NB15和CICIDS 2017上的准确率均达到99%以上，且F1-score分别为99.5%和99.7%。与基线模型相比，提出的IRFD在准确率、精确率和F1-score性能上均有较大提升。

Abstract

In order to improve the accuracy of intrusion detection

considering the advantages of autoencoders in learning features and the mature application of residual networks in constructing deep models

an improved residual network intrusion detection model based on feature dimensionality reduction (IRFD) was proposed. The goal of the proposed IRFD was to solve the issue of low detection accuracy of traditional machine learning based intrusion detection models. In IRFD

the stacking denoising sparse autoencoder was employed to reduce the dimensionality of features and extract effective features. The convolutional attention mechanism was used to improve the residual network and form a classification network that could extract key features. Two typical intrusion detection datasets were employed to verify the detection performance of the IRFD. Experimental results demonstrate that the detection accuracy of the proposed IRFD on the both UNSW-NB15 and CICIDS 2017 datasets are over 99%

with F1-score of 99.5% and 99.7%

respectively. Compared with the state-of-the-art models for the intrusion detection

the accuracy

precision

and F1-score performance of the IRFD were significantly improved.

关键词

Keywords

references

吴昊 , 郝佳佳 , 卢云龙 . 物联网场景下基于蜜场的分布式网络入侵检测系统研究 [J ] . 通信学报 , 2024 , 45 ( 1 ): 106 - 118 .

WU H , HAO J J , LU Y L . Research on distributed network intrusion detection system for IoT based on honeyfarm [J ] . Journal on Communications , 2024 , 45 ( 1 ): 106 - 118 .

刘奇旭 , 肖聚鑫 , 谭耀康 , 等 . 工业互联网流量分析技术综述 [J ] . 通信学报 , 2024 , 45 ( 8 ): 221 - 237 .

LIU Q X , XIAO J X , TAN Y K , et al . Survey of industrial Internet traffic analysis technology [J ] . Journal on Communications , 2024 , 45 ( 8 ): 221 - 237 .

刘涛涛 , 付钰 , 王坤 , 等 . 基于VAE-CWGAN和特征统计重要性融合的网络入侵检测方法 [J ] . 通信学报 , 2024 , 45 ( 2 ): 54 - 67 .

LIU T T , FU Y , WANG K , et al . Network intrusion detection method based on VAE-CWGAN and fusion of statistical importance of feature [J ] . Journal on Communications , 2024 , 45 ( 2 ): 54 - 67 .

FARNAAZ N , JABBAR M A . Random forest modeling for network intrusion detection system [J ] . Procedia Computer Science , 2016 , 89 : 213 - 217 .

NUO Y . A novel selection method of network intrusion optimal route detection based on naive Bayesian [J ] . International Journal of Applied Decision Sciences , 2018 , 11 ( 1 ): 1 - 17 .

赵文灏 , 陈曦 . 增强支持向量机和遗传算法的WSN安全研究 [J ] . 计算机应用与软件 , 2024 , 41 ( 2 ): 300 - 304, 327 .

ZHAO W H , CHEN X . Research on WSN security based on enhanced support vector machine and genetic algorithm [J ] . Computer Applications and Software , 2024 , 41 ( 2 ): 300 - 304, 327 .

肖衡 , 龙草芳 . 基于机器学习的无线传感网络通信异常入侵检测技术 [J ] . 传感技术学报 , 2022 , 35 ( 5 ): 692 - 697 .

XIAO H , LONG C F . Communication anomaly intrusion detection technology for wireless sensor networks based on machine learning [J ] . Chinese Journal of Sensors and Actuators , 2022 , 35 ( 5 ): 692 - 697 .

程超 , 武静凯 , 陈梅 . 一种基于RBM-SVM算法的无线传感网络入侵检测算法 [J ] . 计算机应用与软件 , 2022 , 39 ( 5 ): 325 - 329 .

CHENG C , WU J K , CHEN M . An intrusion detection algorithm for wireless sensor network based on RBM-SVM algorithm [J ] . Computer Applications and Software , 2022 , 39 ( 5 ): 325 - 329 .

杨晓文 , 张健 , 况立群 , 等 . 融合CNN-BiGRU和注意力机制的网络入侵检测模型 [J ] . 信息安全研究 , 2024 , 10 ( 3 ): 202 - 208 .

YANG X W , ZHANG J , KUANG L Q , et al . A network intrusion detection model integrating CNN-BiGRU and attention mechanism [J ] . Journal of Information Security Research , 2024 , 10 ( 3 ): 202 - 208 .

张明明 , 刘凯 , 李贤慧 , 等 . 基于深度学习的恶意行为检测与识别模型研究 [J ] . 信息安全研究 , 2023 , 9 ( 12 ): 1152 - 1158 .

ZHANG M M , LIU K , LI X H , et al . Research on malicious behavior detection and identification model based on deep learning [J ] . Journal of Information Security Research , 2023 , 9 ( 12 ): 1152 - 1158 .

谢胜利 , 陈泓达 , 高军礼 , 等 . 基于分布对齐变分自编码器的深度多视图聚类 [J ] . 计算机学报 , 2023 , 46 ( 5 ): 945 - 959 .

XIE S L , CHEN H D , GAO J L , et al . Deep multi-view clustering based on distribution aligned variational autoencoder [J ] . Chinese Journal of Computers , 2023 , 46 ( 5 ): 945 - 959 .

贺瑞芳 , 赵堂龙 , 刘焕宇 . 基于去噪图自编码器的无监督社交媒体文本摘要 [J ] . 软件学报 , 2024 : 1 - 21 .

HE R F , ZHAO T L , LIU H Y . Denoising graph auto-encoder for unsupervised social media text summarization [J ] . Journal of Software , 2024 : 1 - 21 .

张甜甜 , 赵庶旭 , 王小龙 . 基于堆叠降噪自编码器的肝癌亚型分类 [J ] . 计算机应用与软件 , 2024 , 41 ( 6 ): 79 - 84 .

ZHANG T T , ZHAO S X , WANG X L . Classification of liver cancer subtypes based on stacked denoising autoencoder [J ] . Computer Applications and Software , 2024 , 41 ( 6 ): 79 - 84 .

ALMUTLAQ S , DERHAB A , HASSAN M M , et al . Two-stage intrusion detection system in intelligent transportation systems using rule extraction methods from deep neural networks [J ] . IEEE Transactions on Intelligent Transportation Systems , 2023 , 24 ( 12 ): 15687 - 15701 .

KHAN I A , MOUSTAFA N , PI D C , et al . An enhanced multi-stage deep learning framework for detecting malicious activities from autonomous vehicles [J ] . IEEE Transactions on Intelligent Transportation Systems , 2022 , 23 ( 12 ): 25469 - 25478 .

施媛波 . 变分自编码器和注意力机制的异常入侵检测方法 [J ] . 重庆邮电大学学报(自然科学版) , 2022 , 34 ( 6 ): 1071 - 1078 .

SHI Y B . Anomaly intrusion detection method based on variational autoencoder and attention mechanism [J ] . Journal of Chongqing University of Posts and Telecommunications (Natural Science Edition) , 2022 , 34 ( 6 ): 1071 - 1078 .

ENGELEN G , RIMMER V , JOOSEN W . Troubleshooting an intrusion detection dataset: the CICIDS2017 case study [C ] // Proceedings of the 2021 IEEE Security and Privacy Workshops (SPW) . Piscataway : IEEE Press , 2021 : 7 - 12 .

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