面向雾计算的个性化轻量级分布式网络入侵检测系统

叶天鹏; 林祥; 李建华; 张轩凯; 许力文

doi:10.11959/j.issn.2096-109x.2023035

您当前的位置：

首页 >

文章列表页 >

面向雾计算的个性化轻量级分布式网络入侵检测系统

学术论文 | 更新时间：2024-06-03

- 面向雾计算的个性化轻量级分布式网络入侵检测系统
- Personalized lightweight distributed network intrusion detection system in fog computing
- 网络与信息安全学报 2023年9卷第3期页码：28-37
- 作者机构：
- 作者简介：
  
  [ "叶天鹏（1993- ），男，江苏无锡人，上海交通大学博士生，主要研究方向为车辆云计算、人工智能技术在网络安全领域的应用、自然语言处理" ]
  [ "林祥（1979- ），男，福建闽侯人，上海交通大学工程师，主要研究方向为网络空间安全、自然语言处理、网络安全大数据挖掘" ]
  [ "李建华（1965- ），男，江西九江人，上海交通大学教授、博士生导师，主要研究方向为网络空间、个人隐私保护、数据安全" ]
  [ "张轩凯（1999- ），男，四川广安人，上海交通大学硕士生，主要研究方向为二进制代码分析、物联网安全设备漏洞挖掘以及人工智能与安全" ]
  [ "许力文（1999- ），男，山东泰安人，上海交通大学硕士生，主要研究方向为网络安全态势感知" ]
- 基金信息：
  
  中央高校基本科研业务费专项资金;The Fundamental Research Funds for the Central Universities”(23X010200978);信息网络安全公安部重点实验室（公安部第三研究所）开放课题;Open Subject of the Ministry of Public Security Key Laboratory of Information Network Security (The Third Re-search Institute of the Ministry of Public Security)(C20608)
- DOI：10.11959/j.issn.2096-109x.2023035
  中图分类号： TP393
- 网络首发：2023-06，
  
  纸质出版：2023-06-25
- 稿件说明：
移动端阅览
叶天鹏, 林祥, 李建华, 等. 面向雾计算的个性化轻量级分布式网络入侵检测系统[J]. 网络与信息安全学报, 2023,9(3):28-37.

Tianpeng YE, Xiang LIN, Jianhua LI, et al. Personalized lightweight distributed network intrusion detection system in fog computing[J]. Chinese Journal of Network and Information Security, 2023, 9(3): 28-37.
叶天鹏, 林祥, 李建华, 等. 面向雾计算的个性化轻量级分布式网络入侵检测系统[J]. 网络与信息安全学报, 2023,9(3):28-37. DOI： 10.11959/j.issn.2096-109x.2023035.

Tianpeng YE, Xiang LIN, Jianhua LI, et al. Personalized lightweight distributed network intrusion detection system in fog computing[J]. Chinese Journal of Network and Information Security, 2023, 9(3): 28-37. DOI： 10.11959/j.issn.2096-109x.2023035.

摘要

随着物联网技术不断发展，低时延高动态大带宽的新型物联网应用不断出现。这些需求导致海量设备和信息广泛聚集在网络边缘，因而推动了雾计算架构的出现和深入发展。而随着雾计算架构的广泛深入应用，为了保障其安全所部署的分布式网络安全架构也面临着雾计算本身所带来的挑战，如雾计算节点计算和网络通信资源的局限性以及雾计算应用的高动态性限制了复杂网络入侵检测算法的边缘化部署。为了有效解决上述问题，提出了一个面向雾计算架构的个性化轻量级分布式网络入侵检测系统（PLD-NIDS）。该系统基于卷积神经网络架构训练大规模复杂网络流入侵检测模型，同时进一步采集各雾计算节点的网络流量类型分布情况，提出个性化模型蒸馏算法和基于加权一阶泰勒近似剪枝算法对复杂模型进行快速个性化压缩，突破了传统模型压缩算法在面对大量个性化节点时由于压缩计算开销过大而只能提供单一压缩模型用于边缘节点部署的局限性。根据实验结果，所提的PLD-NIDS架构能够实现边缘入侵检测模型的快速个性化压缩。与传统模型剪枝算法相比，所提出的架构在计算损耗和模型精度上取得了较好平衡。在模型精度上，所提的加权一阶泰勒近似剪枝算法与传统一阶泰勒近似剪枝算法相比，在同样的 0.2%模型精度损失条件下能够提升约4%的模型压缩比。

Abstract

With the continuous development of Internet of Things (IoT) technology

there is a constant emergency of new IoT applications with low latency

high dynamics

and large bandwidth requirements.This has led to the widespread aggregation of massive devices and information at the network edge

promoting the emergence and deep development of fog computing architecture.However

with the widespread and in-depth application of fog computing architecture

the distributed network security architecture deployed to ensure its security is facing critical challenges brought by fog computing itself

such as the limitations of fog computing node computing and network communication resources

and the high dynamics of fog computing applications

which limit the edge deployment of complex network intrusion detection algorithms.To effectively solve the above problems

a personalized lightweight distributed network intrusion detection system (PLD-NIDS) was proposed based on the fog computing architecture.A large-scale complex network flow intrusion detection model was trained based on the convolutional neural network architecture

and furthermore the network traffic type distribution of each fog computing node was collected.The personalized model distillation algorithm and the weighted first-order Taylor approximation pruning algorithm were proposed to quickly compress the complex model

breaking through the limitation of traditional model compression algorithms that can only provide single compressed models for edge node deployment due to the high compression calculation overhead when facing a large number of personalized nodes.According to experimental results

the proposed PLD-NIDS architecture can achieve fast personalized compression of edge intrusion detection models.Compared with traditional model pruning algorithms

the proposed architecture achieves a good balance between computational loss and model accuracy.In terms of model accuracy

the proposed weighted first-order Taylor approximation pruning algorithm can achieve about 4% model compression ratio improvement under the same 0.2% model accuracy loss condition compared with the traditional first-order Taylor approximation pruning algorithm.

关键词

Keywords

references

MANIMURUGAN S . IoT-fog-cloud model for anomaly detection using improved Naïve Bayes and principal component analysis [J ] . Journal of Ambient Intelligence and Humanized Computing , 2021 : 1 - 10 .

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

BUCZAK A L , GUVEN E . A survey of data mining and machine learning methods for cyber security intrusion detection [J ] . IEEE Communications Surveys ＆ Tutorials , 2015 , 18 ( 2 ): 1153 - 1176 .

THANIGAIVELAN N K , NIGUSSIE E , KANTH R K , et al . Distributed internal anomaly detection system for Internet-ofThings [C ] // Proceedings of 2016 13th IEEE Annual Consumer Communications ＆ Networking Conference (CCNC) . 2016 : 319 - 320 .

DIRO A , CHILAMKURTI N . Leveraging LSTM networks for attack detection in fog-to-things communications [C ] // Proceedings of IEEE Communications Magazine . 2018 : 124 - 130 .

DIRO A A , CHILAMKURTI N . Distributed attack detection scheme using deep learning approach for Internet of things [J ] . Future Generation Computer Systems , 2018 , 82 : 761 - 768 .

LAWAL M A , SHAIKH R A , HASSAN S R . An anomaly mitigation framework for IoT using fog computing [J ] . Electronics , 2020 , 9 ( 10 ): 1565 .

MALEH Y , ABDELLAHEZZATI W . Lightweight intrusion detection scheme for wireless sensor networks [J ] . International Journal of Computer Science , 2015 , 42 : 1 - 8 .

AN X S , ZHOU X W , LYU X , et al . Sample selected extreme learning machine based intrusion detection in fog computing and MEC [J ] . Wireless Communications and Mobile Computing , 2018 , 2018 : 1 - 10 .

DAO T N , LEE H . Stacked autoencoder-based probabilistic feature extraction for on-device network intrusion detection [J ] . IEEE Internet of Things Journal , 2022 , 9 ( 16 ): 14438 - 14451 .

NGUYEN X H , NGUYEN X D , HUYNH H H , et al . Realguard:a lightweight network intrusion detection system for IoT gateways [J ] . Sensors (Basel,Switzerland) , 2022 , 22 ( 2 ): 432 .

彭媛媛 . 基于一维卷积神经网络的轻量级物联网入侵检测方法研究 [D ] . 成都:电子科技大学 , 2022 .

PENG Y Y . A lightweight IoT intrusion detection method based on one-dimensional convolutional neural network [D ] . Chengdu:University of Electronic Science and Technology of China , 2022 .

THAKKAR A , LOHIYA R . A review on machine learning and deep learning perspectives of IDS for IoT:recent updates,security issues,and challenges [J ] . Archives of Computational Methods in Engineering , 2021 , 28 ( 4 ): 3211 - 3243 .

ZHAO R J , LI Z J , XUE Z , et al . A novel approach based on lightweight deep neural network for network intrusion detection [C ] // Proceedings of 2021 IEEE Wireless Communications and Networking Conference (WCNC) . 2021 : 1 - 6 .

CHOUDHARY T , MISHRA V , GOSWAMI A , et al . A comprehensive survey on model compression and acceleration [J ] . Artificial Intelligence Review , 2020 , 53 ( 7 ): 5113 - 5155 .

GONG Y C , LIU L , YANG M , et al . Compressing deep convolutional networks using vector quantization [J ] . arXiv Preprint arXiv:1412.6115 , 2014 .

GUPTA S , AGRAWAL A , GOPALAKRISHNAN K , et al . Deep learning with limited numerical precision [C ] // Proceedings of the 32nd International Conference on Machine Learning . 2015 : 1737 - 1746 .

VANHOUCKE V , SENIOR A , MAO M . Improving the speed of neural networks on CPUs [C ] // Proceedings of Deep Learning and Unsupervised Feature Learning Workshop . 2011 : 1 - 8 .

HASSIBI B , STORK D G . Second order derivatives for network pruning:optimal brain surgeon [C ] // Proceedings of Advances in Neural Information Processing Systems . 1992 : 164 - 171 .

HAN S , POOL J , TRAN J , et al . Learning both weights and connections for efficient neural networks [C ] // Proceedings of the 28th International Conference on Neural Information Processing Systems . 2015 : 1135 - 1143 .

LI H , KADAV A , DURDANOVIC I , et al . Pruning filters for efficient ConvNets [J ] . arXiv Preprint arXiv:1608.08710 , 2016 .

IANDOLA F N , HAN Song , MOSKEWICZ M W , et al . SqueezeNet:AlexNet-level accuracy with 50x fewer parameters and＜ 0.5 MB model size [J ] . arXiv Preprint arXiv:1602.07360 , 2016 .

HOWARD A G , ZHU M L , CHEN B , et al . MobileNets:efficient convolutional neural networks for mobile vision applications [J ] . arXiv Preprint arXiv:1704.04861 , 2017 .

HINTON G , VINYALS O , DEAN J . Distilling the knowledge in a neural network [J ] . arXiv Preprint arXiv:1503.02531 , 2015 .

ROMERO A , BALLAS N , KAHOU S E , et al . FitNets:Hints for thin deep nets [J ] . arXiv Preprint arXiv:1412.6550 , 2014 .

MOLCHANOV P , TYREE S , KARRAS T , et al . Pruning convolutional neural networks for resource efficient inference [C ] // Proceedings of 5th International Conference on Learning Representations (ICLR) . 2019 .

MOLCHANOV P , MALLYA A , TYREE S , et al . Importance estimation for neural network pruning [C ] // Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . 2020 : 11256 - 11264 .

WANG W , ZHU L Q , GUO B Q . Reliable identification of redundant kernels for convolutional neural network compression [J ] . Journal of Visual Communication and Image Representation , 2019 ,63:102582.

浏览量

260

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

FL-IoV：基于联邦学习的综合车联网入侵检测方法

基于改进Cycle-GAN的样本数据生成在入侵检测中的应用研究

基于异构执行体同步控制的入侵检测模型研究

联邦学习通信优化方法综述

基于轻量级梯度提升机优化的工业互联网入侵检测方法