Network intrusion detection by using cellular neural network with Tabu Search

Zhongxue Yang*, Adem Karahoca, Ning Yang, Nizamettin Aydin

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Citations (Scopus)

Abstract

This paper presents a novel Cellular Neural Network (CNN) templates learning approach based on Tabu Search (TS) for detecting network intrusions. The TS method was applied to CNN with symmetric templates and was verified by simulations. Simulation experiments on intrusion detection have shown that the TS-based template learning algorithm exhibits superior performance in computation time to find the optimal solution and in the solution quality in contrast to the algorithm of genetic algorithm (GA) and simulated annealing (SA).

Original languageEnglish
Title of host publicationProceedings BLISS 2008 - 2008 ECSIS Symposium on Bio-inspired, Learning, and Intelligent Systems for Security
Pages64-68
Number of pages5
DOIs
Publication statusPublished - 2008
Externally publishedYes
Event2008 ECSIS Symposium on Bioinspired, Learning, and Intelligent Systems for Security, BLISS-2008 - Edinburgh, United Kingdom
Duration: 4 Aug 20086 Aug 2008

Publication series

NameProceedings BLISS 2008 - 2008 ECSIS Symposium on Bio-inspired, Learning, and Intelligent Systems for Security

Conference

Conference2008 ECSIS Symposium on Bioinspired, Learning, and Intelligent Systems for Security, BLISS-2008
Country/TerritoryUnited Kingdom
CityEdinburgh
Period4/08/086/08/08

Fingerprint

Dive into the research topics of 'Network intrusion detection by using cellular neural network with Tabu Search'. Together they form a unique fingerprint.

Cite this