An Implementation of Intrusion Detection System Based on Genetic Algorithm
Keywords:
Intrusion Detection, Genetic Algorithm, NSL KDD, MATLABAbstract
The intrusion detection downside is turning into a difficult task attributable to the proliferation of heterogeneous networks since the raised property of systems provides larger access to outsiders and makes it easier for intruders to avoid identification. Intrusion observation systems are accustomed detect unauthorized access to a system. By This paper I am going to present a survey on intrusion detection techniques that use genetic rule approach. Currently Intrusion Detection System (IDS) that is outlined as an answer of system security is used to spot the abnormal activities during a system or network. To this point completely different approaches are utilized in intrusion detections, however regrettably any of the systems isn't entirely ideal. Hence, the hunt of improved technique goes on. During this progression, here I even have designed AN Intrusion Detection System (IDS), by applying genetic rule (GA) to expeditiously observe numerous styles of the intrusive activities among a network. The experiments and evaluations of the planned intrusion detection system are performed with the NSL KDD intrusion detection benchmark dataset. The experimental results clearly show that the planned system achieved higher accuracy rate in distinctive whether or not the records are traditional or abnormal ones and obtained cheap detection rate.
References
R.Elamaran and R.Mala, “A Study on Network Intrusion Detection System (NIDSs) In Virtual Network Structure”, International Journal of Computer Sciences and Engineering (IJCSE), Vol. 03, Issue - 11,November-2015, pp.59 – 164.
Mostaque Md. Morshedur Hassan, LCB College, Maligaon, Guwahati, Assam, India,“Current Studies on Intrusion Detection System, Genetic Algorithm and Fuzzy Logic”, International Journal of Distributed and Parallel Systems, Vol. 4, Issue-2, March-2013,pp.35-47.
Y.Dhanalakshmi and Dr. I. Ramesh Babu, “Intrusion Detection Using Data Mining Along Fuzzy Logic and Genetic Algorithms”, Dept. of Computer Science & Engineering , Acharya nagarjuna University, Guntur, A.P. India International Journal of Computer Science & Network Security (IJCSNS), Vol.8,Issue-2,Februry-2008,pp.27-32.
Zorana Banković, José M. Moya, Álvaro Araujo, Slobodan Bojanić and Octavio Nieto-Taladriz, “A Genetic Algorithm based Solution for Intrusion Detection”, Journal of Information Assurance and Security(JIAS), Vol.4 , Issue-3 ,June-2009, pp.192-199.
Shelly Xiaonan Wu, Wolfgang Banzhaf, “The use of computational intelligence in intrusion detection systems: a review”, Applied Soft Computing, Vol.10, Issue-01, January-2010, pp.1–35.
Mohammad Sazzadul Hoque, Md. Abdul Mukit & Md. Abu Naser Bikas, “An Implementation of Intrusion Detection System using Genetic Algorithm” , Department of Computer Science and Engineering, Shahjalal University of Science and Technology, Sylhet, Bangladesh, International Journal of Network Security and Its Applications (IJNSA),Vol.4,Issue-2,March-2012, pp.109-120.
S Selvakani Kandeeban, and Rengan S Rajesh , Department of Computer Applications, Jaya Engineering College1 Chennai, Tamilnadu, 602 024, India. Dept. of CSE, MS University, Tirunelveli, Tamilnadu, 627 009, India,“Integrated Intrusion Detection System using Soft computing”, International Journal of Network Security, Vol.10, Issue-2, March -2010,pp.87-92.
W. Lu and I. Traore, Department of Electrical and Computer Engineering, University of Victoria, Victoria B.C., Canada “Detecting New Forms of Network Intrusion Using Genetic Programming”, Computational Intelligence, vol. 20, Issue-03, August -2004, pp.475-494.
K. Burbeck & N.Y. Simmin (2007), Department of Computer and Information Science, Linkoping University ,Sweden,“Adaptive Real-Time Anomaly Detection with Incremental Clustering”, Information Security Technical Report, Vol. 12, Issue- 1 ,07-March- 2007,pp.56–67.
T.S. Chou, K.K. Yen & J. Luo , “Network Intrusion Detection Design using Feature Selection of Soft Computing Paradigms”, International Journal of Computational Intelligence, Vol. 4, Issue-3, 2008, pp.196–208.
Bhavani M. Thuraisingham,Latifur Khan, Mamoun Awad , University of Texas at Dallas, Dallas , USA,“A New Intrusion Detection System using Support Vector Machines and Hierarchical Clustering”, The International Journal on Very Large Data Bases, Vol.16, Issue-04,October-2007,pp.507–521.
S. M. Aqil Burney,M. Sadiq Ali Khan and Jawed Naseem,Department of Computer Science ,University of Karachi , Pakistan, “Efficient Probabilistic Classification Methods for NIDS”, (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8 ,Issue-08 , November-2010, pp.168-172.
Baoyi Wang; Feng Li; Shaomin Zhang, “Research onIntrusion Detection Based on Campus Network”, Intelligent Information Technology Application, Vol.01, 2009, pp.468-471.
Downloads
Published
How to Cite
Issue
Section
License

This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.
