基于SVM的网络入侵检测集成学习算法

计算机科学 ›› 2014, Vol. 41 ›› Issue (2): 197-200.

基于SVM的网络入侵检测集成学习算法

谭爱平,陈浩,吴伯桥

湖南大学信息科学与工程学院长沙410082;湖南大学信息科学与工程学院长沙410082;湖南大学信息科学与工程学院长沙410082

出版日期:2018-11-14 发布日期:2018-11-14
基金资助:
本文受国家自然科学基金项目(61272190)资助

Network Intrusion Intelligent Detection Algorithm Based on AdaBoost

TAN Ai-ping,CHEN Hao and WU Bo-qiao

Online:2018-11-14 Published:2018-11-14

摘要/Abstract

摘要： 互联网络中,计算机和设备随时受到恶意入侵的威胁,严重影响了网络的安全性。入侵行为升级快、隐蔽性强、随机性高,传统方法难以有效防范。针对这一问题,提出一种基于SVM的网络入侵检测集成学习算法,该算法利用SVM建立入侵检测基学习器,采用AdaBoost集成学习方法对基学习器迭代训练,生成最终的入侵检测模型,仿真实验表明了该算法的有效性。

关键词: 安全,集成学习,入侵检测,AdaBoost,SVM 中图法分类号TP273文献标识码A

Abstract: In the Internet,computers and equipment are threaded by malicious intrusion,and the safety of network is seriously affected．Intrusion behavior has features of upgraded fast,strong concealment,random characteristics,so the traditional methods are difficult to prevent this problem effectively．In this paper,a network intrusion intelligent detection algorithm based on AdaBoost was presented．The SVM is used to build the learning-module of intrusion detection．The AdaBoost is used for training these learning-modules,and generating the final the intrusion detection model．The simulation results show the effectiveness of the algorithm.

Key words: Security,Integrated learning,Intrusion detection,AdaBoost,SVM

谭爱平,陈浩,吴伯桥. 基于SVM的网络入侵检测集成学习算法[J]. 计算机科学, 2014, 41(2): 197-200. https://doi.org/

TAN Ai-ping,CHEN Hao and WU Bo-qiao. Network Intrusion Intelligent Detection Algorithm Based on AdaBoost[J]. Computer Science, 2014, 41(2): 197-200. https://doi.org/

参考文献

[1] Amor N,Benferhat S,Elouedi Z．Naive Bayes VS Decision Trees in Intrusion Detection System[C]∥Proceedings of the 2004ACM Symposium on Applied Computing．2004:420-424
[2] Sebring M,Shellhouse M,Hanna E．Expert systems in intrusion detection:a case study[C]∥Proceedings of the 11th National Computer Security Conference．Baltimore,Maryland,2006:74-81
[3] Fuchsberger A．Intrusion Detection Systems and Intrusion Prevention Systems[J].Information Security Technical Report,2005,34(10):134-139
[4] Hu Y,Perrig A,Johnson D．Wormhole attacks in wireless net-works[J]．IEEE Journal on Selected Areas in Communications,2006,24(2):370-380
[5] 王振树,李林川,牛丽．基于贝叶斯证据框架的SVM负荷建模[J]．电工技术学报,2009,24(8):83-86
[6] Davy M,Desobry F,Arthur G．An online support vector ma-chine for abnormal events detection[J]．Signal Processing,2006,86(8):2009-2025
[7] Bo Cui-mei．Research on the modeling method based on eliding time window for support vector machine soft-sensing [J]．Automatic Instrument,2006,27(1):45-51
[8] 李良敏,温广瑞,王生昌．基于遗传算法的回归型SVM参数选择法[J]．计算机工程与应用,2008,44(7):23-26
[9] 颜根廷,李传江,马广富．基于混合遗传算法的SVM参数选择[J]．哈尔滨工业大学学报,2008,40(5):134-138
[10] 陈爱斌,夏利民,赵桂敏．基于Boosting方法的人脸检测[J]．计算机工程与应用,2004,27(3):48-52
[11] 吴飞,庄永真,潘红．基于分形布朗运动和AdaBoosting的多类音频例子识别[J]．计算机研究与发展,2003,18(7):62-65
[12] Diao Li-li,Hu Ke-yun,Lu Yu-chang．Improved Stumps Combined by boosting for Text Categorization[J]．Journal of Software,2002,25(8):51-53
[13] 夏利民,戴汝为．基于Boosting模糊分类的滚动轴承故障诊断[J]．模式识别与人工智能,2003,18(3):72-75

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed