An Efficient Intrusion Detection System Based on GA to Recognize Attacks in User Privileges


(*) Corresponding author


Authors' affiliations


DOI's assignment:
the author of the article can submit here a request for assignment of a DOI number to this resource!
Cost of the service: euros 10,00 (for a DOI)

Abstract


Network Security is an important issue, almost 70% of data security threats are created within the organization. For all the organizations when they share the information from one to another place the threat may occur. Presently the data can be shared on any network are always being a risk of intrusion of attacks. The finding of attacks by using Intrusion Detection System (IDS) against computer networks is becoming a most important problem to resolve in the area of network security. There are various approaches are being utilized for security purpose. In this paper, a genetic algorithm is proposed to identify various detection of intrusion/harmful attack from unauthorized user (External attacks) in addition to attacks by authorized users (Internal attacks) based on the privileges given to them. The algorithm provides many features when the information in such a protocol type like Time duration, security, maintenance in categorization on rule set. The rule set is valid up to specific type of attacks.  The training dataset are used to create the set of rules to recognize the type of attacks in the networks using fitness function. The dataset training has completed on the KDD99 datasets and Network overflow is taken which will reduce the complexity of security.
Copyright © 2013 Praise Worthy Prize - All rights reserved.

Keywords


Intrusion Detection System (IDS); Attacks; Genetic Algorithm; KDD99 Dataset; Rule Set

Full Text:

PDF


References


M. Botha, R. Solms, “Utilizing Neural Networks For Effective Intrusion Detection”, ISSA, 2004.

D. Zamboni, “Using Internal Sensors For Computer Intrusion Detection”. Center for Education and Research in Information Assurance and Security, Purdue University. August 2001.

A. Ozun, A. Cifter, Aided-Computer Evaluation of Nonlinear Combination of Financial Forecast with Genetic Algorithm, (2007) International Review on Computers and Software (IRECOS), 2 (3), pp. 276 – 284.

Chen, R., Zhou, J., Liu, C., Zhang, Y., A novel detection model for network attack inspired by immunology, (2012) International Review on Computers and Software (IRECOS), 7 (6), pp. 2927-2932.

W. Li, “Using Genetic Algorithm for Network Intrusion Detection”. “A Genetic Algorithm Approach to Network Intrusion Detection”. SANS Institute, USA, 2004.

W. Lu, I. Traore, “Detecting New Forms of Network Intrusion Using Genetic Programming”. Computational Intelligence, vol. 20, pp. 3, Blackwell Publishing, Malden, pp. 475-494, 2004.

S. Peddabachigari, Ajith Abraham, C. Grosan, J. Thomas, “Modeling intrusion detection system using hybrid intelligent systems”, Journal of Network and Computer Applications, Volume 30, Issue 1, January 2007, Pages 114–132.

Tao Peng, C. Leckie, Kotagiri Ramamohanarao, “Information sharing for distributed intrusion detection systems”, Journal of Network and Computer Applications, Volume 30, Issue 3, August 2007, Pages 877–899.

Karen Scarfone, Peter Mell “Guide to Intrusion Detection and Prevention Systems (IDPS) (Draft)”, National Institute of Standards and Technology Special Publication 800-94 Revision 1 (Draft), Natl. Inst. Stand. Technol. Spec. Publ. 800-94 Rev. 1, 111 pages (Jul. 2012).

Sengan, S., Chethur Pandian, S., An efficient agent-based intrusion detection system for detecting malicious nodes in MANET routing, (2012) International Review on Computers and Software (IRECOS), 7 (6), pp. 3037-3041.

Zougagh, H., Toumanari, A., Latif, R., Idboufker, N., A new solution to defend against cooperative black hole attack in optimized link state routing protocol, (2013) International Review on Computers and Software (IRECOS), 8 (2), pp. 519-526.

Balasubramaniyan JS, Garcia-Fernanasez JO, Isaco D, Spatford E, Zamboni ,“An architecture for intrusion detection using autonomous agents”, Proceedings of 14th annual computer security applications conference, 1998.

Heberlein LT, Mukherjee B, Levitt K N, Mansur DL, “Towards Detecting Intrusions in a Networked Environment”, Proceedings of 14th department of energy computer security group conference, 1991.

B. Uppalaiah, K. Anand, B. Narsimha, S. Swaraj, T. Bharat, “Genetic Algorithm Approach to Intrusion Detection System”, International Journal of Computer Sci ence And Technology, Vol. 3, Iss ue 1, Jan. - March 2012, ISSN : 0976-8491 (Online) | ISSN : 2229-4333 (Print).

S. Selvakani and R.S. Rajesh, “Genetic Algorithm for Framing Rules for Intrusion Detection” IJCSNS International Journal of Computer Science and Network Security, Vol. 7 No. 11, November 2007.

Wei Li, “Using Genetic Algorithm for Network Intrusion Detection”, Department of Computer Science and Engineering, Mississippi, State University, Mississippi State, Ms 39762.

A. Kartit, a. Saidi, “ A New Approach to Intrusion Detection System", Journal of Theoretical and Applied Information Technology, Vol. 36 No.2, ISSN: 1992-8645, E-ISSN: 1817-3195, 2012.

Ren Hui Gong, Mohammad Zulkernine, Purang Abolmaesumi, “A Software Implementation of a Genetic Algorithm Based Approach to Network Intrusion Detection”, Proceedings of the Sixth International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing and First ACIS International Workshop on Self-Assembling Wireless Networks (CNPD/SAWN ’05).

Rahul Malhotra, Narinder Singh & Yaduvir Singh, “Genetic Algorithms: Concepts, Design for Optimization of Process Controllers”, Published by Canadian Center of Science and Education, Vol. 4, No. 2; March 2011, www.ccsenet.org/cis

R.Shanmugavadivu, N. Nagarajan, “Learning of Intrusion Detector in Conceptual Approach of Fuzzy Towards Intrusion Methodology”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 2, Issue 5, May 2012 ISSN: 2277 128X, www.ijarcsse.com

Zorana Bankovic, José M. Moya, “A Genetic Algorithm-based Solution for Intrusion Detection”, Journal of Information Assurance and Security 4 (2009) 192-199, Dynamic Publishers, Inc

J. P. Planquart, “Application of Neural Networks to Intrusion Detection”, SANS Institute Reading Room, (2001).

Mohammad Sazzadul Hoque, Md. Abdul Mukit and Md. Abu Naser Bikas, “An Implementation of Intrusion Detection System using Genetic Algorithm”, International Journal of Network Security & Its Applications (IJNSA), Vol.4, No.2, March 2012.

Tobias Blickle and Lothar Thiele, “A Comparison of Selection Schemes used in Genetic Algorithms, Computer Engineering and Communication Networks Lab (TIK), Swiss Federal Institute of Technology (ETH), Gloriastrasse Zurich, Switzerland TIK- Report, Version 2 Edition, 1995.


Refbacks

  • There are currently no refbacks.



Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2022 Praise Worthy Prize