Research on Network Intrusion Detection Using Support Vector Machines Based on Principal Component Analysis

doi:10.3969/j.issn.1671-1122.2015.02.003

Abstract

Abstract: Aim

ing at the shortcomings of the traditional intrusion detection system, such as low rate of detection, time wasting, high rate of false positives and so on, this paper proposed a method of network intrusion detection (PCA-SVM) using support vector Machines (SVM) based on principal component analysis (PCA). This method begins with data preprocessing, then find the optimal set of attributes by traversing the 41 principal component attribute values, finally training support vector machine classifier to obtain a detector based on this data set. This experiment has been simulated in the Matlab software with the KDD99 data. As a result compared with the traditional intrusion detector which training from 41 attributes, this method greatly reduces the detection time, improve the detection efficiency and reduce the rate of false positives. So it provides a new feasible solution for network intrusion detection technology.

Key words: intrusion detection, principal component analysis, support vector machine, KDD99 data set, attribution reduction

CLC Number:

TP309

QI Ming-yu, LIU Ming, FU Yan-ming. Research on Network Intrusion Detection Using Support Vector Machines Based on Principal Component Analysis[J]. Netinfo Security, 2015, 15(2): 15-18.

Figures/Tables 6

References 14

[1]	杨富华,彭钢.PCA-SVM在网络入侵检测中的仿真研究[J].计算机仿真,2011,28(7):146-149.
[2]	张妮娜. 基于SVM的异常入侵检测系统关键技术的研究[D].青岛:山东科技大学,2008.
[3]	SUNG A H.Identifying Important Features for Intrusion Detection Using Support Vector Machines and Neural Networks[C]// Proceedings of the 2003 Symposium on Applications and the Internet, 2003:209-216.
[4]	钟时. 基于支持向量机的入侵检测系统的研究[D].长春:吉林大学,2008.
[5]	董春曦. 支持向量机及其在入侵检测中的应用研究[D].西安:西安电子科技大学,2004.
[6]	周玖玖. 入侵检测中支持向量机参数选择方法研究[D].长沙:中南大学,2012.
[7]	李陶深,钟淑瑛. 基于Matlab的主成分分析方法PCA的实现[J]. 广西大学学报,2005,30(8):74-77.
[8]	代红,刘磊.基于数据筛选策略的入侵检测研究[J].计算机工程与设计,2012,33(2):488-492.
[9]	李汉彪,刘渊. 一种SVM入侵检测的融合新策略[J].计算机工程与应用,2012,48(4):87-90.
[10]	赵志宇,黎蔚. 入侵检测系统现状分析[J].电脑知识与技术2013,9(22):5061-5063.
[11]	周志德,李汉彪.基于属性约简和SVM参数优化的入侵检测方法[J]. 计算机应用与软件,2011,28(12):60-63.
[12]	唐忠,曹俊月.基于粗糙集属性约简的SVM异常入侵检测方法[J]. 通信技术,2009,42(2):261-263.
[13]	姚旭,王晓丹,张玉玺,等.特征选择方法综述[J].控制与决策,2012,27(2):162-166.
[14]	张雪琴,顾春华,吴吉义.异常检测中支持向量基最优模型选择方法[J].电子科技大学学报,2011,40(04):559-563.

[1]	ZHANG Hao, XIE Dazhi, HU Yunsheng, YE Junwei. A Review of Network Anomaly Detection Based on Semi-Supervised Learning [J]. Netinfo Security, 2024, 24(4): 491-508.
[2]	JIANG Rong, LIU Haitian, LIU Cong. Unsupervised Network Intrusion Detection Method Based on Ensemble Learning [J]. Netinfo Security, 2024, 24(3): 411-426.
[3]	FENG Guangsheng, JIANG Shunpeng, HU Xianlang, MA Mingyu. New Research Progress on Intrusion Detection Techniques for the Internet of Things [J]. Netinfo Security, 2024, 24(2): 167-178.
[4]	JIN Zhigang, DING Yu, WU Xiaodong. Federated Intrusion Detection Algorithm with Bilateral Correction Merging Gradient Difference [J]. Netinfo Security, 2024, 24(2): 293-302.
[5]	SUN Hongzhe, WANG Jian, WANG Peng, AN Yulong. Network Intrusion Detection Method Based on Attention-BiTCN [J]. Netinfo Security, 2024, 24(2): 309-318.
[6]	HU Jinhua. Network Traffic Detection Technology for Railway Ticketing System [J]. Netinfo Security, 2024, 24(1): 143-149.
[7]	SHEN Hua, TIAN Chen, GUO Sensen, MU Zhiying. Research on Adversarial Machine Learning-Based Network Intrusion Detection Method [J]. Netinfo Security, 2023, 23(8): 66-75.
[8]	PENG Hanzhong, ZHANG Zhujun, YAN Liyue, HU Chenglin. Research on Intrusion Detection Mechanism Optimization Based on Federated Learning Aggregation Algorithm under Consortium Chain [J]. Netinfo Security, 2023, 23(8): 76-85.
[9]	MA Min, FU Yu, HUANG Kai. A Principal Component Analysis Scheme for Security Outsourcing in Cloud Environment Based on Secret Sharing [J]. Netinfo Security, 2023, 23(4): 61-71.
[10]	LIU Changjie, SHI Runhua. A Smart Grid Intrusion Detection Model for Secure and Efficient Federated Learning [J]. Netinfo Security, 2023, 23(4): 90-101.
[11]	GAO Qingguan, ZHANG Bo, FU Anmin. An Advanced Persistent Threat Detection Method Based on Attack Graph [J]. Netinfo Security, 2023, 23(12): 59-68.
[12]	JIN Zhigang, LIU Kai, WU Xiaodong. A Review of IDS Research in Smart Grid AMI Field [J]. Netinfo Security, 2023, 23(1): 1-8.
[13]	WANG Huazhong, TIAN Zilei. Intrusion Detection Method of ICS Based on Improved CGAN Algorithm [J]. Netinfo Security, 2023, 23(1): 36-43.
[14]	LIU Xiangyu, LU Tianliang, DU Yanhui, WANG Jingxiang. Lightweight IoT Intrusion Detection Method Based on Feature Selection [J]. Netinfo Security, 2023, 23(1): 66-72.
[15]	ZHANG Xinglan, FU Juanjuan. Auxiliary Entropy Reduction Based Intrusion Detection Model for Ordinary Differential Equations [J]. Netinfo Security, 2022, 22(6): 1-8.