INFORMATION CHANGE THE WORLD

International Journal of Computer Network and Information Security(IJCNIS)

ISSN: 2074-9090 (Print), ISSN: 2074-9104 (Online)

Published By: MECS Press

IJCNIS Vol.6, No.9, Aug. 2014

Artificial Intrusion Detection Techniques: A Survey

Full Text (PDF, 264KB), PP.51-57


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Author(s)

Ashutosh Gupta, Bhoopesh Singh Bhati, Vishal Jain

Index Terms

Artificial Neural Network;Genetic Algorithm;Immunity;Intrusion Detection;False Alarm

Abstract

Networking has become the most integral part of our cyber society. Everyone wants to connect themselves with each other. With the advancement of network technology, we find this most vulnerable to breach and take information and once information reaches to the wrong hands it can do terrible things. During recent years, number of attacks on networks have been increased which drew the attention of many researchers on this field. There have been many researches on intrusion detection lately. Many methods have been devised which are really very useful but they can only detect the attacks which already took place. These methods will always fail whenever there is a foreign attack which is not famous or which is new to the networking world. In order to detect new intrusions in the network, researchers have devised artificial intelligence technique for Intrusion detection prevention system. In this paper we are going to cover what types evolutionary techniques have been devised and their significance and modification.

Cite This Paper

Ashutosh Gupta, Bhoopesh Singh Bhati, Vishal Jain,"Artificial Intrusion Detection Techniques: A Survey", IJCNIS, vol.6, no.9, pp.51-57, 2014. DOI: 10.5815/ijcnis.2014.09.07

Reference

[1]http://en.wikipedia.org/wiki/Internet.

[2]Michael E. Whitman, Herbert J. Mattord "Principles of Information Security" pp.

[3]Karthikeyan .K.R and A. Indra "Intrusion Detection Tools and Techniques – A Survey"International Journal of Computer Theory and Engineering, Vol.2, No.6, December, 2010 pp 901-906.

[4]Michael E. Whitman, Herbert J. Mattord "Principles of Information Security" pp.

[5]http://www.sans.org/security-resources/idfaq/statistic_ids.php.

[6]Iftikar Ahmad, Azween B Abdullah, Abdullah S Alghamadi Comparative Analysis of Intrusion Detection Approaches 2012 21th International Conference on Computer Modelling and Simulation pp 586-591.

[7]S. Rajasekaran, Pai G. A. Vijayalakshmi "Neural Networks, Fuzzy Logic, and Genetic Algorithms: Synthesis and Applications" pp.

[8]Mohammad SazzadulHoque, Md. Abdul Mukit and Md. Abu NaserBikas "AN IMPLEMENTATION OF INTRUSION DETECTION SYSTEM USING GENETIC ALGORITHM" International Journal of Network Security & Its Applications (IJNSA), Vol.4, No.2, March 2012 pp 110-120.

[9]Wei Li, Using Genetic Algorithm for Network Intrusion Detection.

[10]AnupGoyal, Chetan Kumar (2008) GA-NIDS: A Genetic Algorithm based Network Intrusion Detection System.

[11]S. Rajasekaran, Pai G. A. Vijayalakshmi "Neural Networks, Fuzzy Logic, and Genetic Algorithms: Synthesis and Applications" pp.

[12]http://library.thinkquest.org/C007395/tqweb/history.html.

[13]Gang Wang, Jinxing Hao,Jian Ma, Lihua Huang "A new approach to intrusion detection using Artificial Neural Networks and fuzzy clustering".

[14]Sang-Jun Han and Sung-Bae Cho "Evolutionary Neural Network for Anomaly Detection Based on the Behaviour of a Program", IEEE Transaction on System, Man, and Cybernetics- Part B CYBRNETICS VOL. 36, NO. 3, JUNE 2006.

[15]Deqiang Zhou,” Optimization Modeling for GM(1,1) Model Based on BP Neural Network ” I. J. Computer Network and Information Security, 2012, 1, 24-30 Published Online February 2012 in MECS (http://www.mecs-press.org/) DOI:10.5815/ijcnis.2012.01.03.

[16]http://en.wikipedia.org/wiki/Immune_system.

[17].http://en.wikipedia.org/wiki/Artificial_immune_system.

[18]ArefEshghiShargh "Using artificial immune system on Implementation of Intrusion Detection System", 2009 Third UKSim European Symposium on Computer Modelling and Simulation.

[19]http://www.artificial-immune- systems.org/algorithms.shtml.

[20]Susan C. Lee, David V. Heinbuch "Training a Neural-Network Based Intrusion Detector to Recognize Novel Attacks" IEEE TRANSACTION ON SYSTEM, MAN AND CYBERNETICS- PART: SYSTEMS AND HUMANS, VOL. 31, NO. 4, JULY 2001.

[21]Chung-Ming Ou, C. R. Ou "Multi-Agent Artificial Immune Systems (MAAIS) for Intrusion Detection: Abstraction from Danger Theory" Agent and Multi-Agent Systems: Technologies and Applications Lecture Notes in Computer Science Volume 5559, 2009, pp 11-19.

[22]Patricia Mostardinha, Bruno Filipe Faria, André Zúquete, FernãoVistulo de Abreu "A Negative Selection Approach to Intrusion Detection" Artificial Immune Systems Lecture Notes in Computer Science Volume 7597, 2012, pp 178-190.

[23]Mahdi Mohammadi, Ahmad Akbari, BijanRaahemi, BabakNassersharif "A Real Time Anomaly Detection System Based on Probabilistic Artificial Immune Based Algorithm" Artificial Immune Systems Lecture Notes in Computer Science Volume 7597, 2012, pp 205-217.