高速铁路信号系统网络入侵检测技术研究  

Study on Network Intrusion Detection Techniquesfor High-speed Railway Signal Systems

作　　者：曹峰[1] 林瑜筠[2] CAO Feng;LIN Yujun(Rail Transit Engineering Practice Center,Nanjing Vocational Institute of Railway Technology,Nanjing 210031,China;Nanjing Vocational Institute of Railway Technology,Nanjing 210031,China)

机构地区：[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

出　　处：《高速铁路技术》2024年第5期67-71,82,共6页High Speed Railway Technology

基　　金：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)。

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。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.

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 

分 类 号：U284[交通运输工程—交通信息工程及控制]