Study of Intrusion Detection Based on Genetic Algorithm and Rule

Li Wen Xu; Li Juan Qiao; Xun Yong Ou; Kun Zhang

doi:10.4028/www.scientific.net/AMM.602-605.1797

Paper Titles

Motion Shadow Detection Method Based on Bagging Algorithm
p.1778

The Design of Yield Monitor Device of Tomato Harvester
p.1782

Summary of Remote Monitoring System for CNC Machines
p.1787

The Research of the Grass-Roots Level Radar Equipment Maintenance and Detecting Expert System Model
p.1793

Study of Intrusion Detection Based on Genetic Algorithm and Rule
p.1797

A New Signal Classification Method Based on EEMD and FCM and its Application in Bearing Fault Diagnosis
p.1803

Fast Computation of Sample Entropy in Forced Oscillation Detection
p.1807

Spatial Distorted Target Recognition Based on Improved Maximum Average Correlation Height Algorithm
p.1811

Studying on the Application of Using Audible Sound to Detect Cavitation Phenomenon in a Centrifugal Pump
p.1815

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 602-605Study of Intrusion Detection Based on Genetic...

Study of Intrusion Detection Based on Genetic Algorithm and Rule

Abstract:

The rapid increase of information technology usage demands the high level of security in order to keep the data resources and equipments of the user secure. In this current era of networks, there is an eventual stipulate for development which is consistent, extensible and easily manageable, with low maintenance cost solutions for Intrusion Detection. Network Intrusion Detection based on rules formulation is an efficient approach to classify various types of attack. DoS or Probing attack are relatively more common, and can be detected more accurately if contributing parameters are formulated in terms of rules. Genetic Algorithm is used to devise such rule. It is found that accuracy of rule based learning increases with the number of iteration.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 602-605)

Pages:

1797-1802

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.602-605.1797

Citation:

Cite this paper

Online since:

August 2014

Authors:

Li Wen Xu*, Li Juan Qiao, Xun Yong Ou, Kun Zhang

Keywords:

Genetic Algorithm (GA), KDD-99, Network Intrusion Detection

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] N. Ye, X. Li, Q. Chen S. M. Emran, and M. Xu. Probabilistic techniques for intrusion detection based on computer audit data[J]. IEEE Trans. SMC-A，vol. 31，Jul. 2001：266–274.

DOI: 10.1109/3468.935043

Google Scholar

[2] Baoyi Wang，Feng Li，Shaomin Zhang. Research on Intrusion Detection Based on Campus Network[J]. Intelligent Information Technology Application，2009 Vol 1：468-471.

Google Scholar

[3] Akyazi. U, Uyar and A.S.E. Distributed Intrusion Detection Using Mobile agenets against DDoS attacks[J]. Computer and Information Sciences 2008, Pages 1-6.

DOI: 10.1109/iscis.2008.4717920

Google Scholar

[4] Nicoletta Dessì and Barbara Pes, An Evolutionary Method for Combining Different Feature Selection Criteria in Microarray Data Classification, Journal of Artificial Evolution and applications, Vol (2009).

DOI: 10.1155/2009/803973

Google Scholar

[5] Zhang Fengbin, Yang Yongtian, etc. Application of genetic algorithm to Intrusion Detection Based on network anomaly[J]. Journal of Economics. 2004, 5: 875-877.

Google Scholar

[6] Luo Ankun, Li Wa. Study on Application of Genetic Algorithm to Network Intrusion Detection System Based on Coarse-grained Model. Jounral of Yunnan University. 2006，28(2)：51-54.

Google Scholar

[7] Guo Han, Cao Yuanda. Diging of Attack Mode in Intrusion Detection System[J]. Jounral of Beijing Institute of Technology. 2003, 23(2): 212-214.

Google Scholar

[8] Wei Yuxin. Research on the Key Technology of Network Intrusion Detection System[D]. Beijing. Beijing University of Posts and Telecommunications. (2008).

Google Scholar