[1]曹峰,林瑜筠.高速铁路信号系统网络入侵检测技术研究[J].高速铁路技术,2024,15(05):67-71,82.[doi:10.12098/j.issn.1674-8247.2024.05.011]
　CAO Feng,LIN Yujun.Study on Network Intrusion Detection Techniques for High-speed Railway Signal Systems[J].HIGH SPEED RAILWAY TECHNOLOGY,2024,15(05):67-71,82.[doi:10.12098/j.issn.1674-8247.2024.05.011]
点击复制

高速铁路信号系统网络入侵检测技术研究()
分享到：

《高速铁路技术》[ISSN:1674-8247/CN:51-1730/U]

卷:
15卷
期数:
2024年05期
页码:
67-71,82
栏目:
研究创新
出版日期:
2024-10-30

文章信息/Info

Title:
Study on Network Intrusion Detection Techniques for High-speed Railway Signal Systems
文章编号:
1674-8247(2024)05-0067-05
作者:
曹峰1; 林瑜筠2
(1.南京铁道职业技术学院轨道交通工程实践中心, 南京 210031; 2. 南京铁道职业技术学院, 南京 210031)
Author(s):
CAO Feng1; LIN Yujun2
(1. Rail Transit Engineering Practice Center, Nanjing Vocational Institute of Railway Technology,Nanjing 210031, China; 2. Nanjing Vocational Institute of Railway Technology, Nanjing 210031, China)
关键词:
信号系统;  入侵检测;  机器学习;  KDD CUP 99数据集;  朴素贝叶斯;  逻辑回归
Keywords:
signal systems;  intrusion detection;  machine learning;  KDD CUP 99 dataset;  Naive Bayes;  logistic regression
分类号:
U284
DOI:
10.12098/j.issn.1674-8247.2024.05.011
文献标志码:
A
摘要:
入侵检测作为一种网络主动防御技术,能够有效阻止来自黑客的多种手段攻击。随着机器学习的发展,相关技术也开始应用到入侵检测中。本文采用sklearn库中preprocessing模块的函数对KDD CUP 99数据集进行预处理,基于朴素贝叶斯和逻辑回归算法,建立了网络入侵检测模型,并利用信息增益算法对入侵相关特征进行选择,然后进行训练与预测。实验结果表明,选择特征子集进行训练和预测能够保证预测准确率并大幅提高检测效率。研究成果可为高速铁路信号系统网络入侵检测模型的设计和建立提供参考。
Abstract:
Intrusion detection, as an active defense mechanism in networking, effectively thwarts diverse forms of attacks by hackers. With the advancements in machine learning, related technologies are increasingly being employed in intrusion detection systems. This study utilized preprocessing functions from the sklearn library's preprocessor module to preprocess the KDD CUP 99 dataset. Based on Naive Bayes and logistic regression algorithms, a network intrusion detection model was constructed, followed by feature selection using the information gain algorithm prior to training and prediction. Experimental results demonstrate that training and predicting with a subset of selected features ensures prediction accuracy while significantly boosting detection efficiency. The findings provide valuable reference for the design and establishment of network intrusion detection models in high-speed railway signal systems.

参考文献/References:

[1] 余超, 雷雳. 铁路移动终端安全管控方案探讨[J]. 高速铁路技术, 2022, 13(5): 10-13, 30.
YU Chao, LEI Li. Discussion on Safety Control Solution of Railway Mobile Terminals[J]. High Speed Railway Technology, 2022, 13(5): 10-13, 30.
[2] 赖英旭, 刘增辉, 蔡晓田, 等. 工业控制系统入侵检测研究综述[J]. 通信学报, 2017, 38(2): 143-156.
LAI Yingxu, LIU Zenghui, CAI Xiaotian, et al. Research on Intrusion Detection of Industrial Control System[J]. Journal on Communications, 2017, 38(2): 143-156.
[3] 张蕾, 崔勇, 刘静, 等. 机器学习在网络空间安全研究中的应用[J]. 计算机学报, 2018, 41(9): 1943-1975.
ZHANG Lei, CUI Yong, LIU Jing, et al. Application of Machine Learning in Cyberspace Security Research[J]. Chinese Journal of Computers, 2018, 41(9): 1943-1975.
[4] 张玉清, 董颖, 柳彩云, 等. 深度学习应用于网络空间安全的现状、趋势与展望[J]. 计算机研究与发展, 2018, 55(6): 1117-1142.
ZHANG Yuqing, DONG Ying, LIU Caiyun, et al. Situation, Trends and Prospects of Deep Learning Applied to Cyberspace Security[J]. Journal of Computer Research and Development, 2018, 55(6): 1117-1142.
[5] 杨印根, 王忠洋. 基于深度神经网络的入侵检测技术[J]. 网络安全技术与应用, 2019(4): 37-41.
YANG Yingen, WANG Zhongyang. Intrusion Detection Technology Based on Deep Neural Network[J]. Network Security Technology & Application, 2019(4): 37-41.
[6] 解滨, 李清扬, 董新玉. 面向网络入侵检测数据的对抗样本生成方法[J]. 山东大学学报(理学版), 2021, 56(3): 28-36.
XIE Bin, LI Qingyang, DONG Xinyu. Adversarial Examples Generation Method for Network Intrusion Detection Data[J]. Journal of Shandong University(Natural Science), 2021, 56(3): 28-36.
[7] 王晓程, 刘恩德, 谢小权. 攻击分类研究与分布式网络入侵检测系统[J]. 计算机研究与发展, 2001, 38(6): 727-734.
WANG Xiaocheng, LIU Ende, XIE Xiaoquan. Attack Classification Research and a Distributed Network Intrusion Detection System[J]. Journal of Computer Research and Development, 2001, 38(6): 727-734.
[8] 丁龙斌, 伍忠东, 苏佳丽. 基于集成深度森林的入侵检测方法[J]. 计算机工程, 2020, 46(3): 144-150.
DING Longbin, WU Zhongdong, SU Jiali. Intrusion Detection Method Based on Ensemble Deep Forests[J]. Computer Engineering, 2020, 46(3): 144-150.
[9] 李勇, 张波. 一种基于深度CNN的入侵检测算法[J]. 计算机应用与软件, 2020, 37(4): 324-328.
LI Yong, ZHANG Bo. An Intrusion Detection Algorithm Based on Deep Cnn[J]. Computer Applications and Software, 2020, 37(4): 324-328.
[10] 曹峰. 计算机联锁系统安全评估分析与研究[J]. 高速铁路技术, 2015, 6(4): 1-3.
CAO Feng. Analysis and Research on Safety Assessment of Computer Interlocking System[J]. High Speed Railway Technology, 2015, 6(4): 1-3.
[11] 张玲, 张建伟, 桑永宣, 等. 基于随机森林与人工免疫的入侵检测算法[J]. 计算机工程, 2020, 46(8): 146-152.
ZHANG Ling, ZHANG Jianwei, SANG Yongxuan, et al. Intrusion Detection Algorithm Based on Random Forest and Artificial Immunity[J]. Computer Engineering, 2020, 46(8): 146-152.
[12] KDD Cup 1999 Data. Irvine, CA(USA), Information and Computer Science University of California, Irivine[EB/OL]. http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html, 2005-6-8.
[13] 张全龙. 基于深度学习模型的网络入侵检测研究[D]. 天津: 天津理工大学, 2021.
ZHANG Quanlong. Research on Network Intrusion Detection Based on Deep Learning Model[D].Tianjin: Tianjin University of Technology, 2021.
[14] WANG Wei, HE Yongzhong, LIU Jiqiang, et al. Constructing Important Features from Massive Network Traffic for Lightweight Intrusion Detection[J]. IET Information Security, 2015, 9(6): 374-379.

备注/Memo

备注/Memo:
收稿日期:2020-03-15
作者简介:曹峰,(1987-),男,工程师。
基金项目:教育部高铁安全协同创新中心、江苏省高铁安全工程技术研究开发中心科研项目(GTAQ202204)
更新日期/Last Update: 2024-10-30