Asian Journal of Computer Science and Technology (AJCST)
A Literature Survey on the Network Security and Intrusion Detection System Using Data Mining Techniques
Author : R. Dharmarajan and V. ThiagarasuVolume 8 No.1 January-March 2019 pp 7-12
Abstract
Network security has become more important to personal computer users, organizations, and the military. With the advent of the internet, security became a major concern and the history of security allows a better understanding of the emergence of security technology. The entire field of network security is vast and in an evolutionary stage. The range of study encompasses a brief history dating back to internet’s beginnings and the current development in network security. In order to understand the research being performed today, background knowledge of the importance of security, types of attacks in the networks. This paper elaborates the literature study on network security in various domains. Finally, it summarizes the research directions by literature survey.
Keywords
Network Security, Cloud Computing, Sensor Networks, Ad Hoc Networks, Internet of Things
Full Text:
References
[1] Shi-Jinn Horng, Ming-Yang Su, Yuan-Hsin Chen, Tzong-Wann Kao, Rong-Jian Chen, Jui- Lin Lai, Citra Dwi Perkasa, “A novel intrusion detection system based on hierarchical clustering and support vector machines”, Elsevier Computer Network, pp.306–313, 2010.
[2] Mohammad Wazid, “Hybrid Anomaly Detection using K-Means Clustering in Wireless Sensor Networks”, Center for Security, Theory and Algorithmic Research, pp. 1-17, 2014.
[3] Y.-J. Shen and M.-S. Wang, “Broadcast scheduling in wireless sensor networks using fuzzy hopfield neural network,” Expert Systems with Applications, Vol. 34, No. 2, pp. 900-907, 2008
[4] Y. Wang, M. Martonosi, and L.-S. Peh, “Predicting link quality using supervised learning in wireless sensor networks,” ACM SIGMOBILE Mobile Computing and Communications Review, Vol. 11, No. 3, pp. 71–83, 2007
[5] Mohit Malik, Namarta kapoor, Esh naryan, Aman Preet Singh, “Rule Based Technique detecting Security attack for Wireless Sensor network using fuzzy logic”, International Journal of Advanced Research in Computer Engineering & Technology, Vol. 1, No. 4,, ISSN: 2278–1323, June 2012.
[6] Reda M. Elbasiony, Elsayed A. Sallam, Tarek E. Eltobely,Mahmoud M. Fahmy, “A hybrid network intrusion detection framework based on random forests and weighted k-means” Ain Shams Engineering Journal, vol 4, pp.753–762,2013.
[7] Levent Koc, Thomas A. Mazzuchi, Shahram Sarkani, “A network intrusion detection system based on a Hidden Naïve Bayes multiclass classifier”, Elsevier, pp.13492–13500, 2012.
[8] Wenying Fenga, Qinglei Zhangc, Gongzhu Hud, Jimmy Xiangji Huange, “Mining network data for intrusion detection through combining SVMs with ant colony networks”, Elsevier, pp. 127-140, 2013.
[9] Megha Bandgar, Komal dhurve, Sneha Jadhav,Vicky Kayastha,Prof. T.J Parvat, “Intrusion Detection System using Hidden Markov Model (HMM)”, IOSR Journal of Computer Engineering (IOSRJCE) e-ISSN: 2278-0661, p- ISSN: 2278- 8727Vol. 10, No. 3, pp. 66-70, Mar. – Apr. 2013.
[10] Dat Tran, Wanli Ma, and Dharmendra Sharma, “Network Anomaly Detection using Fuzzy Gaussian Mixture Models”, International Journal of Future Generation Communication and Networking, pp.37- 42, 2012.
[11] Vahid Golmah, “An Efficient Hybrid Intrusion Detection System based on C5.0 and SVM”, International Journal of Database Theory and Application Vol.7, No.2, pp. 59-70, 2014.
[12] Punam Mulak, Nitin R. Talhar, “Novel Intrusion Detection System Using Hybrid Approach”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 4, No. 11, ISSN: 2277 128X, November 2014.
[13] Venkata Suneetha Takkellapati1, G.V.S.N.R.V Prasad, “Network Intrusion Detection system based on Feature Selection and Triangle area Support Vector Machine”, International Journal of Engineering Trends and Technology, Vol. 3, No. 2012.
[14] Vaishali Kosamkar, Sangita S Chaudhari, “Improved Intrusion Detection System using C4.5Decision Tree and Support Vector Machine”, International Journal of Computer Science and Information Technologies, Vol. 5, No. 2, pp. 1463- 1467, 2014.
[15] Harmeet Kaur, Ravneet Kaur, “Crossbreed Routing Protocol for SPEED Terminology in Wireless Sensor Networks”, International Journal of Advance Research in Computer Science and management Studies, Vol. 2, No. 7, ISSN: 2321-7782, July 2014..
[16] H. Oh, I. Doh and K. Chae, “Attack classification based on data mining technique and its application for reliable medical sensor communication”, International Journal of Computer Science and Applications, Vol. 6, No. 3, pp. 20-32, 2009.
[17] [17] N. Ye and X. Li, “A Scalable Clustering Technique for Intrusion Signature Recognition”, Proceedings of 2001 IEEE Workshop on Information Assurance and Security, 2001.
[18] G. Singh, F. Masseglia, C. Fiot, A. Marascu and P. Poncelet, “Data Mining for Intrusion Detection: from Outliers to True Intrusions”, The 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD’09), Thailand, 2009.
[19] K. Faraoun and A. Boukelif, “Genetic Programming Approach for Multi-Category Pattern Classification Applied to Network Intrusions Detection”, The International Arab Journal of Information Technology, Vol. 4, No. 3, 2007.
[20] W. Lee, S. Stolfo, P. Chan, E. Eskin, W. Fan, M. Miller, S. Hershkop and J. Zhang, “Real Time Data Mining-based Intrusion Detection”, Proceedings of DISCEX II, June 2001.
[21] K. Ioannis, T. Dimitriou and F. C. Freiling, “Towards Intrusion Detection in Wireless Sensor Networks”, 13th European Wireless Conference, Paris, April 2007.
[22] D. Farid, J. Darmont, N. Harbi, N. Hoa and M. Rahman, “Adaptive Network Intrusion Detection Learning: Attribute Selection and Classification”, International Conference on Computer Systems Engineering (ICCSE 09), Bangkok, Thailand, December 2009.
[23] J. Zhang and M. Zulkernine, “Anomaly Based Network Intrusion Detection with Unsupervised Outlier Detection”, Symposium on Network Security and Information Assuranc-Proc. of the IEEE International Conference on Communications (ICC), Istanbul, Turkey, June, 2006.
[24] M. Tavallaee, E. Bagheri, W. Lu and A. Ghorbani, “A Detailed Analysis of the KDD’99 CUP Data Set”, The 2nd IEEE Symposium on Computational Intelligence Conference for Security and Defense Applications (CISDA), 2009.
[25] M. Campos and B. Milenova, “Creation and Deployment of Data Mining-Based Intrusion Detection Systems in Oracle Database 10g”, an online document at http://www.oracle.com/technology/ products/ bi/odm/pdf/odm_based_intrusion_detection_paper_1205.pdf.
[26] Prothives and S. Srinoy, “Integrating ART and Rough Set Approach for Computer Security”, Proceedings of the International Multi Conference of Engineers and Computer Scientists, Vol. 1, 2009.
[27] H. Güneş Kayacık, A. Nur Zincir-Heywood and M. I. Heywood, “Selecting features for intrusion detection: a feature relevance analysis on KDD’99 intrusion detection datasets”, Third Annual Conference on Privacy, Security and Trust, October 2005.
[28] M. Amini and R. Jalili, “Network-based intrusion detection using unsupervised adaptive resonance theory (ART)”, Proceedings of the fourth conference on engineering of intelligent systems (EIS 2004), Madeira, Portugal, 2004.
[29] J. Xiao and H. Song, “A Novel Intrusion Detection Method Based on Adaptive Resonance Theory and Principal Component Analysis”, Proceedings of the 2009 International Conference on Communications and Mobile Computing, Vol. 3, 2009.
[30] [30] Skoudis, Ed, and Tom Liston, “Counter hack reloaded: a step-by-step guide to computer attacks and effective defenses”, Prentice Hall Press, 2005.
[31] K. Labib and V. Rao Vemuri, “Detecting Denial-of-Service And Network Probe Attacks Using Principal Component Analysis”, In Third Conference on Security and Network Architectures, La Londe, (France), 2004.
[32] T. Eldos, M. Khubeb Siddiqui and A. Kanan “On the KDD’99 Dataset: Statistical Analysis for Feature Selection”, Journal of Data Mining and Knowledge Discovery, 2012.