A Review on effect of SVM in Intrusion Detection System
DOI:
https://doi.org/10.26438/ijcse/v6i12.471474Keywords:
Data mining, Intrusion Detection System, SVMAbstract
Intrusion detection system is a system combining both software and hardware that monitors and analysis huge volume of network traffic and detects malicious activities. The role of IDS in system security is significant but not sufficient. Data analysis is a part of the IDS process. Data mining is a data analytic tool. If it is integrated with IDS, performance of IDS will be elevated. One of the data mining classification algorithms is SVM. It is widely applied in IDS. In this paper a methodical study on SVM in IDS was done. This paper reports the effect of SVM in IDS. It is observed that SVM increases the performance of IDS and also it has some limitations. This review provides new ways for further research to overcome these limitations.
References
[1] Jamal Hussain, Aishwarya Mishra “An Effective Intrusion Detection Framework Based On Support Vector Machine Using Nsl - KDD Dataset”, in Indian Journal of Computer Science and Engineering (IJCSE), e-ISSN : 0976-5166, Vol. 8 No. 6 PP: 703 -713, Dec 2017-Jan 2018
[2] Jiapu Zhang, “A Complete List of Kernels Used in Support Vector Machines” in Biochemistry & Pharmacology: Open Access, DOI: 10.4172/2167-0501.1000195, PP: 4-5. 2015
[3] Jayshree Jha, Leena Ragha, “ Intrusion Detection System using Support Vector Machine”, in International Journal of Applied Information Systems (IJAIS) – ISSN : 2249-0868,PP:25-30,2013.
[4] Minakshi Bisen1, Amit Dubey2, “An Intrusion Detection System Based On Support Vector Machine Using Hierarchical Clustering And Genetic Algorithm” in International Journal Of Engineering And Computer Science ISSN:2319-7242, Volume 4 Issue 1, PP: 10062-10064, January 2015.
[5] Zhenlong Li, Qingzhou Zhang and Xiaohua Zhao, “Performance analysis of K-nearest neighbor, support vector machine, and artificial neural network classifiers for driver drowsiness detection with different road geometries” in International Journal of Distributed Sensor Networks 2017, Vol. 13(9) PP:1-9, 2017
[6] Sandeep Ranode, “Intrusion Detection System Using SVM Classification” in International Journal of Innovative Research in Computer and Communication Engineering, ISSN(Online): 2320-9801, Vol. 4, Issue 6, PP: 12180- 12184, June 2016.
[7] Liliya Demidova ,Evgeny Nikulchev ,Yulia Sokolova , “ Big Data Classification Using the SVM Classifiers with the Modified Particle Swarm Optimization and the SVM Ensembles”, in International Journal of Advanced Computer Science and Applications (IJACSA), Vol. 7, No. 5, PP: 294-31, 2016
[8] Lei Shi1, Qiguo Duan2, Xinming Ma1, and Mei Weng1, “The Research of Support Vector Machine in Agricultural Data Classification” in IFIP International Federation for Information Processing 2012, CCTA 2011, Part III, IFIP AICT 370, PP: 265–269, 2012.
[9] Vitthal Manekar1, Kalyani Waghmare2, “Intrusion Detection System using Support Vector Machine and Particle Swarm Optimization (PSO)” in International Journal of Advanced Computer Research (ISSN (print): 2249-7277 ISSN (online): 2277-7970) Volume-4 Number-3 Issue-PP: 808-812, 16 September-2014.
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