Application of improved interactive genetic algorithm incorporated with SVM in intrusion detection

doi:10.3778/j.issn.1002-8331.2010.29.058

Computer Engineering and Applications ›› 2010, Vol. 46 ›› Issue (29): 200-202.DOI: 10.3778/j.issn.1002-8331.2010.29.058

• 图形、图像、模式识别 • Previous Articles Next Articles

Application of improved interactive genetic algorithm incorporated with SVM in intrusion detection

KAN Yuan¹，LIU Yi-an²，XUE Xiao²，WEI Min³

School of Information Engineering，Jiangnan University，Wuxi，Jiangsu 214122，China

Received:2009-03-03 Revised:2009-05-04 Online:2010-10-11 Published:2010-10-11
Contact: KAN Yuan

结合SVM的交互式遗传算法在入侵检测中的应用

阚媛¹，刘以安²，薛潇²，魏敏³

江南大学信息工程学院，江苏无锡 214122

通讯作者: 阚媛

Abstract

Abstract: For the problems with few samples，a lot of characteristics and difficult to incorporate the practical experience and the existing algorithm，this paper applies the interactive genetic algorithm into intrusion detection technology，incorporates the characteristic of SVM，then designs the improved classified recognition algorithm.The experiment result proves that the improved interactive genetic algorithm incorporated with SVM in intrusion detection domain is effective，feasible，and can obtain very good detection rate.

Key words: Interactive Genetic Algorithm（IGA）, Support Vector Machine（SVM）, intrusion detection

摘要： 针对入侵检测中存在样本少、特征多、难于将实际经验与现有算法有机结合的问题，将交互式遗传算法应用到入侵检测技术中，并结合SVM的特点，设计出改进后的分类识别算法。实验证明，将SVM与交互式遗传算法相结合应用于入侵检测领域中，算法有效、可行，而且能获得很好的检测率。

关键词: 交互式遗传算法, 支持向量机, 入侵检测

CLC Number:

TP393.08

KAN Yuan¹，LIU Yi-an²，XUE Xiao²，WEI Min³. Application of improved interactive genetic algorithm incorporated with SVM in intrusion detection[J]. Computer Engineering and Applications, 2010, 46(29): 200-202.

阚媛¹，刘以安²，薛潇²，魏敏³. 结合SVM的交互式遗传算法在入侵检测中的应用[J]. 计算机工程与应用, 2010, 46(29): 200-202.

[1]	HAN Weiyu, CHENG Longsheng. Research on Roling Bearing Failure Mode Classification Based on MTS and SVM [J]. Computer Engineering and Applications, 2021, 57(6): 239-246.
[2]	WANG Zhendong, ZHANG Lin, LI Dahai. Survey of Intrusion Detection Systems for Internet of Things Based on Machine Learning [J]. Computer Engineering and Applications, 2021, 57(4): 18-27.
[3]	WEN Jiebin, YANG Wenzhong, MA Guoxiang, ZHANG Zhihao, LI Hailei. Micro-expression Recognition Based on Apex Frame Optical Flow and Convolutional Autoencoder [J]. Computer Engineering and Applications, 2021, 57(4): 127-133.
[4]	LI Junxia, ZHANG Qin, ZHENG Guimei. Overview of Human Posture Recognition by Ultra-wideband Radar [J]. Computer Engineering and Applications, 2021, 57(3): 14-23.
[5]	XU Xianfeng, CAI Lulu, ZHANG Li. Photovoltaic Power Generation Prediction Algorithm Based on MLP and DBN [J]. Computer Engineering and Applications, 2021, 57(3): 266-272.
[6]	CHEN Fujian, XIE Weixin, XIA Ting. Adaptive Anti-occlusion Target Tracking Algorithm Based on LCT+ [J]. Computer Engineering and Applications, 2021, 57(22): 190-198.
[7]	CAO Lei, LI Zhanbin, YANG Yongsheng, ZHAO Longfei. Intrusion Detection Method Based on Two-Layer Attention Networks [J]. Computer Engineering and Applications, 2021, 57(19): 142-149.
[8]	SHEN Shaoyu, CAI Manchun, LU Tianliang, ZHAO Qi. Intrusion Detection Algorithm based on LFKPCA-DWELM [J]. Computer Engineering and Applications, 2021, 57(17): 130-137.
[9]	GONG Junhui, HU Xiaohui, DU Yongwen. Research on Selection of Optimal Defense Strategy Based on Evolutionary Game [J]. Computer Engineering and Applications, 2021, 57(13): 116-123.
[10]	CHEN Feiyu, YUE Wenbin, RAO Yinglu, XING Jinhao, MA Xiaojing. Autonomous Precision Landing of Drone Based on Improved TLD Algorithm [J]. Computer Engineering and Applications, 2020, 56(7): 247-254.
[11]	LI Jiayue, ZHAO Bo, LI Xiang, LIU Hui, LIU Yifan, ZOU Jianwen. Network Traffic Anomaly Prediction Method Based on Deep Learning [J]. Computer Engineering and Applications, 2020, 56(6): 39-50.
[12]	MA Ling, LUO Xiaoshu, JIANG Pinqun. Research on Dot Matrix Character Recognition Based on Template Matching and Support Vector Machine [J]. Computer Engineering and Applications, 2020, 56(4): 134-139.
[13]	ZHANG Zhonglin, FENG Yibang, ZHAO Zhongkai. Oversampling Method for Unbalanced Data Sets Based on SVM [J]. Computer Engineering and Applications, 2020, 56(23): 220-228.
[14]	CHEN Hong, WANG Runting, XIAO Chenglong, GUO Pengfei, HUANG Jie, CHEN Honglin. Research on Intrusion Detection Model Based on DBN-XGBDT [J]. Computer Engineering and Applications, 2020, 56(22): 83-91.
[15]	DI Chong, LI Tong. Network Unknown Attack Detection with Deep Learning [J]. Computer Engineering and Applications, 2020, 56(22): 109-116.

Application of improved interactive genetic algorithm incorporated with SVM in intrusion detection

结合SVM的交互式遗传算法在入侵检测中的应用

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics