Research on Rule Extraction Technology Based on Genetic Algorithm in Intrusion Detection

Hui Ling Guo

doi:10.4028/www.scientific.net/AMR.760-762.857

Paper Titles

Research on Wells Monitoring Background Management Software Based on Labview
p.836

Design and Analysis of a New Dynamic Tracking Performance Testing System
p.841

Study of Fiber Grating Strain Sensing to On-Line Monitoring for Optical Cable of Electric Power System
p.846

Research on Rule Extraction Technology Based on Genetic Algorithm in Intrusion Detection
p.857

On the Security of a Secure Batch Verification Scheme for VANET
p.862

Test Methods Research and Verify for FPGA
p.867

Feasibility Study of Photoacoustic Imaging for Monitoring Temperature in Photothermal Therapy
p.872

The Application of Parameterization in Human Behavior Analysis
p.876

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 760-762Research on Rule Extraction Technology Based on...

Research on Rule Extraction Technology Based on Genetic Algorithm in Intrusion Detection

Abstract:

It is necessary to establish the rule base before intrusion detection. An adaptive method based on genetic algorithms was presented for learning the intrusion detection rules in order to realize the automation of attack rule generation. The genetic algorithm is employed to derive a set of classification rules from network audit data, and the support-confidence framework is utilized as fitness function to judge the quality of each rule. The generated rules are then used to detect or classify network intrusions in a real-time environment.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 760-762)

Pages:

857-861

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.760-762.857

Citation:

Cite this paper

Online since:

September 2013

Authors:

Hui Ling Guo

Keywords:

Feature Extraction, Genetic Algorithm (GA), Intrusion Detection, Misuse Detection

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] CHITTUR A. Model generation for an intrusion detection System using genetic algorithms [J]. http: /www1. cs. col －umbia. edu/ids/publications/gaids-thesis01. pdf , (2005).

Google Scholar

[2] LI W. A genetic algorithm approach to network intrusion detection [J]. SANS Institute, USA, (2004).

Google Scholar

[3] PILLAI M M, ELOFF J H P, VENTER H S. An approach to implement a network intrusion detection system using genetic algorithms[A]. Proceedings of SAICSIT[C]. 2004: 221-228.

Google Scholar

[4] Marinakis Y, Marinaki M. A Hybrid Multi-swarm Particle Swarm Optimization Algorithm for the Probabilistic Traveling Salesman Problem[J]. Computers & Operations Research, 2010, 37(3): 432-442.

DOI: 10.1016/j.cor.2009.03.004

Google Scholar

[5] GOMEZ J, DASGUPTA D. Evolving fuzzy classifiers for intrusion detection [J]. Proceedings of the IEEE, (2002).

Google Scholar

[6] MIDDLEMISS M, DICK G. Feature selection of intrusion detection data using a hybrid genetic algorithm/KNN approach [J]. Design and application of hybrid intelligent systems, IOS Press Amsterdam, 2003: 519-527.

Google Scholar

[7] Mingxia Hu. Intrusion Detection Algorithm Based on BP Neural Network. Computer Engineering, March 2012, Vol. 38(6): 148-150 (In chinese).

Google Scholar

[8] KDD99. KDD99 cup dataset[DB/OL]. http: /kdd. ics. uci. edu /databases /kddcup99, (1999).

Google Scholar