一种基于深度可分离卷积和注意力机制的入侵检测方法

张志飞; 刘峰; 葛祎阳; 李烁; 张煜; 熊轲

doi:10.11959/j.issn.2096-3750.2023.00307

您当前的位置：

首页 >

文章列表页 >

一种基于深度可分离卷积和注意力机制的入侵检测方法

理论与技术 | 更新时间：2024-08-16

- 一种基于深度可分离卷积和注意力机制的入侵检测方法
- An intrusion detection method based on depthwise separable convolution and attention mechanism
- 物联网学报 2023年7卷第1期页码：49-59
- 作者机构：
  
  1. 北京交通大学计算机与信息技术学院高速铁路网络管理教育部工程研究中心，北京 100044
  2. 北京交通大学轨道交通安全协同创新中心，北京 100044
  3. 北京交通大学移动专用网络国家工程研究中心，北京 100044
  4. 国网能源研究院有限公司，北京 102209
- 作者简介：
  
  [ "张志飞（1971- ），男，博士，北京交通大学计算机与信息技术学院高级工程师，主要研究方向为无线通信、网络安全等" ]
  [ "刘峰（1998– ），男，北京交通大学计算机与信息技术学院硕士生，主要研究方向为网络安全、入侵检测、深度学习等" ]
  [ "葛祎阳（1997− ），男，北京交通大学计算机与信息技术学院博士生，主要研究方向为深度学习、强化学习、信息年龄、无线能量传输网络和6G网络等" ]
  [ "李烁（1998- ），男，北京交通大学计算机与信息技术学院硕士生，主要研究方向为可靠传输、拥塞控制、强化学习等" ]
  [ "张煜（1983− ），男，博士，国网能源研究院有限公司高级研究员，主要研究方向为边缘计算、无线协作网络和能源互联网等" ]
  [ "熊轲（1981− ），男，博士，北京交通大学计算机与信息技术学院教授、副院长，主要研究方向为无线协作网络、无线移动网络和网络信息理论等" ]
- 基金信息：
  
  中央高校基本科研业务费资助项目;The Fundamental Research Funds for the Central Universities(2022JBZY021);国家自然科学基金项目(62071033)
- DOI：10.11959/j.issn.2096-3750.2023.00307
  中图分类号： TN915.08
- 纸质出版日期：2023-03-30，
  
  网络出版日期：2023-03，
- 稿件说明：
移动端阅览
张志飞, 刘峰, 葛祎阳, 等. 一种基于深度可分离卷积和注意力机制的入侵检测方法[J]. 物联网学报, 2023,7(1):49-59.

ZHIFEI ZHANG, FENG LIU, YIYANG GE, et al. An intrusion detection method based on depthwise separable convolution and attention mechanism. [J]. Chinese journal on internet of things, 2023, 7(1): 49-59.
张志飞, 刘峰, 葛祎阳, 等. 一种基于深度可分离卷积和注意力机制的入侵检测方法[J]. 物联网学报, 2023,7(1):49-59. DOI： 10.11959/j.issn.2096-3750.2023.00307.

ZHIFEI ZHANG, FENG LIU, YIYANG GE, et al. An intrusion detection method based on depthwise separable convolution and attention mechanism. [J]. Chinese journal on internet of things, 2023, 7(1): 49-59. DOI： 10.11959/j.issn.2096-3750.2023.00307.

摘要

为提高网络入侵检测中多分类的准确率，提出了一种基于深度可分离卷积和注意力机制的入侵检测方法。该方法通过深度可分离卷积、长短期记忆网络组成级联结构，提高了模型对数据中空间和时间特征的提取能力；进一步融入混合域注意力机制完善特征提取过程，提高了模型的检测能力。为了解决在中小样本上检测率低的问题，设计了一种基于变分自编码器和生成对抗网络的数据平衡策略，能有效应对网络数据集的数据不平衡现象，提升了所提检测方法的适应性。在CICIDS-2017、NSL-KDD和UNSW-NB15数据集上的实验结果表明，所提方法能够取得99.80%、99.32%、83.87%的准确率，检测准确率分别提高了0.6%、0.5%、2.3%。

Abstract

In order to improve the accuracy of multi-classification in network intrusion detection

an intrusion detection method was proposed based on depthwise separable convolution and attention mechanism.By constructing a cascade structure combining depthwise separable convolution and long-term and short-term memory networks

the spatial and temporal features of network traffic data can be better extracted.A mixed-domain attention mechanism was introduced to enhance the detection performance.To solve the problem of low detection rate in some samples

a data balance strategy based on the combination of the variational auto-encoder (VAE) the generative adversarial network (GAN) and was designed

which can effectively cope with imbalanced datasets and improve the adaptability of the proposed detection method.The experimental results show that the proposed method is able to achieve 99.80%

99.32%

and 83.87% accuracy on the CICIDS-2017

NSL-KDD and UNSW-NB15 datasets

which is improved by 0.6%

0.5%

and 2.3%

respectively.

关键词

深度学习入侵检测注意力机制生成对抗网络

Keywords

deep learningintrusion detectionattention mechanismgenerative adversarial network

references

LIU H Y, LANG B . Machine learning and deep learning methods for intrusion detection systems:a survey[J]. Applied Sciences. 2019,9(20): 4396-4420.

RADOGLOU-GRAMMATIKIS P I, SARIGIANNIDIS P G . An anomaly-based intrusion detection system for the smart grid based on CART decision tree[C]// Proceedings of 2018 Global Information Infrastructure and Networking Symposium (GIIS). Piscataway:IEEE Press, 2018: 1-5.

任晓奎, 缴文斌, 周丹 . 基于粒子群的加权朴素贝叶斯入侵检测模型[J]. 计算机工程与应用, 2016,52(7): 122-126.

REN X K, JIAO W B, ZHOU D . Intrusion detection model of weighted navie Bayes based on particle swarm optimization algorithm[J]. Computer Engineering and Applications, 2016,52(7): 122-126.

LOPEZ-MARTIN M, CARRO B, SANCHEZ-ESGUEVILLAS A ,et al. Conditional variational autoencoder for prediction and feature recovery applied to intrusion detection in IoT[J]. Sensors (Basel,Switzerland), 2017,17(9): E1967.

WANG W, SHENG Y Q, WANG J L ,et al. HAST-IDS:learning hierarchical spatial-temporal features using deep neural networks to improve intrusion detection[J]. IEEE Access, 2018,6(99): 1792-1806.

ALTHUBITI S A, JONES E M, ROY K . LSTM for anomaly-based network intrusion detection[C]// Proceedings of 2018 28th International Telecommunication Networks and Applications Conference (ITNAC). Piscataway:IEEE Press, 2018: 1-3.

KANNA P R, SANTHI P . Unified deep learning approach for efficient intrusion detection system using integrated spatial-temporal features[J]. Knowledge-Based Systems, 2021,226:107132.

ASHFAQ KHAN M, KIM Y . Deep learning-based hybrid intelligent intrusion detection system[J]. Computers,Materials ＆ Continua, 2021,68(1): 671-687.

JIA H P, LIU J, ZHANG M ,et al. Network intrusion detection based on IE-DBN model[J]. Computer Communications, 2021,178: 131-140.

SIFRE L, MALLAT S . Rigid-Motion scattering for texture classification[J]. Computer Science, 2014,3559: 501-515.

MNIH V, HEESS N, GRAVES A . Recurrent models of visual attention[C]// Advances in neural information processing systems, 2014(2): 2203-2212.

GOODFELLOW I, POUGET-ABADIE J, MIRZA M ,et al. Generative adversarial nets[J]. Communications of the ACM, 2020,63(11): 139-144.

LEI S W, XIA C H, WANG T B . LCHI:low-order correlation and high-order interaction integrated model oriented to network intrusion detection[J]. Wireless Communications and Mobile Computing,2021, 2021:6830372.

刘烁, 张兴兰 . 基于双重注意力的入侵检测系统[J]. 信息网络安全, 2022,22(1): 80-86.

LIU S, ZHANG X L . Intrusion detection system based on dual attention[J]. Netinfo Security, 2022,22(1): 80-86.

曹磊, 李占斌, 杨永胜 ,等. 基于双层注意力神经网络的入侵检测方法[J]. 计算机工程与应用, 2021,57(19): 142-149.

CAO L, LI Z B, YANG Y S ,et al. Intrusion detection method based on two-layer attention networks[J]. Computer Engineering and Applications, 2021,57(19): 142-149.

CHEN Z, LV N, LIU P F ,et al. Intrusion detection for wireless edge networks based on federated learning[J]. IEEE Access, 2020(8): 217463-217472.

SETHI K, MADHAV Y V, KUMAR R ,et al. Attention based multi-agent intrusion detection systems using reinforcement learning[J]. Journal of Information Security and Applications, 2021,61:102923.

FU Y F, DU Y S, CAO Z J ,et al. A deep learning model for network intrusion detection with imbalanced data[J]. Electronics, 2022,11(6): 898.

ANDRESINI G, APPICE A, CAFORIO F P ,et al. ROULETTE:a neural attention multi-output model for explainable Network Intrusion Detection[J]. Expert Systems With Applications, 2022,201:117144.

ZHAO P, FAN Z J, CAO Z W ,et al. Intrusion detection model using temporal convolutional network blend into attention mechanism[J]. International Journal of Information Security and Privacy, 2022,16(1): 1-20.

CAO K, ZHU J Q, FENG W ,et al. Network intrusion detection based on dense dilated convolutions and attention mechanism[C]// Proceedings of 2021 International Wireless Communications and Mobile Computing (IWCMC). Piscataway:IEEE Press, 2021: 463-468.

曹轲, 朱金奇, 马春梅 ,等. 联合多重卷积与注意力机制的网络入侵检测[J]. 天津师范大学学报(自然科学版), 2021,41(3): 75-80.

CAO K, ZHU J Q, MA C M ,et al. Network intrusion detection based on multiple convolutions and attention mechanism[J]. Journal of Tianjin Normal University (Natural Science Edition), 2021,41(3): 75-80.

AHMIM A, MAGLARAS L, FERRAG M A ,et al. A novel hierarchical intrusion detection system based on decision tree and rules-based models[C]// Proceedings of 2019 15th International Conference on Distributed Computing in Sensor Systems (DCOSS). Piscataway:IEEE Press, 2019: 228-233.

DE GREGORIO M, GIORDANO M . An experimental evaluation of weightless neural networks for multi-class classification[J]. Applied Soft Computing, 2018,72: 338-354.

ADNAN M N, ISLAM M Z . Forest PA:constructing a decision forest by penalizing attributes used in previous trees[J]. Expert Systems With Applications, 2017,89: 389-403.

CHANG C C, LIN C J . LIBSVM:a library for support vector machines[J]. ACM Transactions on Intelligent Systems and Technology, 2007: 2-20.

ZHANG X Q, CHEN J H, ZHOU Y ,et al. A multiple-layer representation learning model for network-based attack detection[J]. IEEE Access, 2019(7): 91992-92008.

MOHAMMADI S, NAMADCHIAN A . A new deep learning approach for anomaly base IDS using memetic classifier[J]. International Journal of Computers Communications ＆ Control, 2017,12(5): 677.

QURESHI A U H, LARIJANI H, YOUSEFI M ,et al. An adversarial approach for intrusion detection systems using Jacobian saliency map attacks (JSMA) algorithm[J]. Computers, 2020,9(3): 58.

SUMAIYA THASEEN I, ASWANI KUMAR C . Intrusion detection model using fusion of Chi-square feature selection and multi class SVM[J]. Journal of King Saud University - Computer and Information Sciences, 2017,29(4): 462-472.

丁红卫, 万良, 周康 ,等. 基于深度卷积神经网络的入侵检测研究[J]. 计算机科学, 2019(10): 173-179.

DING H W, WAN L, ZHOU K ,et al. Study on intrusion detection based on deep convolution neural network[J]. Computer Science, 2019(10): 173-179.

ZHANG G L, WANG X D, LI R ,et al. Network intrusion detection based on conditional Wasserstein generative adversarial network and costsensitive stacked autoencoder[J]. IEEE Access, 2020(8): 190431-190447.

YANG Y Q, ZHENG K F, WU C H ,et al. Building an effective intrusion detection system using the modified density peak clustering algorithm and deep belief networks[J]. Applied Sciences, 2019,9(2): 238.

RAJAGOPAL S, KUNDAPUR P P, HAREESHA K S . A stacking ensemble for network intrusion detection using heterogeneous datasets[J]. Security and Communication Networks,2020, 2020:4586875.

PAPAMARTZIVANOS D, GÓMEZ MÁRMOL F, KAMBOURAKIS G . Dendron:genetic trees driven rule induction for network intrusion detection systems[J]. Future Generation Computer Systems, 2018,79: 558-574.

浏览量

533

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

融合多尺度和上下文的无人机救援小目标检测算法

面向6G的生成对抗网络研究进展综述

基于AE和Transformer的运动想象脑电信号分类研究

基于多智能体深度强化学习的多域协同抗干扰方法研究

物联网环境中基于深度学习的差分隐私预算优化方法