Review on Meta Classification Algorithms using WEKA

International Journal of Computer Trends and Technology (IJCTT)          
© 2016 by IJCTT Journal
Volume-35 Number-1
Year of Publication : 2016
Authors : Rausheen Bal, Sangeeta Sharma


Rausheen Bal, Sangeeta Sharma "Review on Meta Classification Algorithms using WEKA". International Journal of Computer Trends and Technology (IJCTT) V35(1):38-47, May 2016. ISSN:2231-2803. Published by Seventh Sense Research Group.

Abstract -
This paper is having a comparative review on different classifiers used for prediction of attack risks on environment having network. In total there are 19 classifiers explained in this paper and the three best or efficient classifiers have been evaluated by three different authors as mentioned in this paper. The data of those three authors has been used in this paper for doing comparison between different classification algorithms. Comparison are taken on the fields of TP-Rate, FP-Rate, Precision, Recall, F-measure etc. Anlaysis was done by those mentioned authors on WEKA tool.

[1] S. Venkata Lakshmi1 and T. Edwin Prabakaran, “Performance Analysis of Multiple Classifiers on KDD Cup Dataset using WEKA Tool” Indian Journal of Science and Technology, Vol 8(17), August 2015
[2] G.Michael, A.Kumaravel and A.Chandrasekar “Detection of malicious attacks by Meta classification algorithms” Int. J. Advanced Networking and Applications Volume: 6 Issue: 5 Pages: 2455-2459 January2015
[3] Pran Dev, Dr. Kulvinder Singh and Dr. Sanjeev Dhawan “Classification of Malicious and Legitimate Nodes for Analysing the Users’ Behaviour in Heterogeneous Online Social Networks” 2015 1st International conference on futuristic trend in computational analysis and knowledge management (ABLAZE 2015) 2015
[4] Langley P. and Simon H. “Applications of machine learning and rule induction”, Communications of the ACM, Vol.38, No. 11, pp. 55–64. 1995
[5] Morgan Kaufmann, San Mateo “C4.5: Programs for Machine Learning.” Quinlan, J.: (1993).
[6] A detunmbi AO, Falaki SO, Adewale OS, Alese BK. “Network Intrusion Detection based on Rough Set and k-Nearest Neighbour”. International Journal of Computing and ICT Research. 2008; 2(1):60–6. Available from: http://www.ijcir. org/volume1number2/article7.pdf
[7] R anjan R, Sahoo G. “A new clustering approach for anomaly intrusion detection.” International Journal of Data Mining and Knowledge Management Process 4(2):29– 38 2014 Mar.
[8] A zad C, Jha VK.” Data Mining based Hybrid Intrusion Detection System.” Indian Journal of Science and Technology. 7(6):781–9. Jun; 2014
[9] K hor K-C, Ting C-Y, Amnuaisuk S-P. “From Feature Selection to Building of Bayesian Classifiers: A Network Intrusion Detection Perspective.” American Journal of Applied Sciences; 6(11):1948–59 2009
[10] L ee W, Stolfo SJ, Mok KW. “Algorithms for Mining System Audit Data.Proc KDD”; 1999.
[11] G hali NI. “Feature Selection for Effective Anomaly Based Intrusion Detection.” International Journal of Computer Science and Network Security. ; 9(3):285–9. 2009 Mar
[12] “K-DD CUP 1999 DATASET”: Available from: http://kdd.ics.
[13] S ANS Institute InfoSec Reading Room.”Understanding Intrusion Detection Systems”; 2001.
[14] W u X, Kumar V, Ross Quinlan RJ, Ghosh J, Yang Q, Motoda H, McLachlan GJ, Ng A, Liu B, Yu PS, Zhou Z-H, Steinbach M, Hand DJ, Steinberg D. Top 10 “algorithms in data mining.” London: Springer-Verlag; 2008. p. 1–3. DOI: 10.1007/ s10115-007-0114-2
[15] V enkata Lakshmi S, Edwin Prabakaran T.” Application of k- Nearest Neighbour Classification Method for Intrusion Detection in Network Data.” International Journal of Computer Applications (0975-8887);; 97(7):34–7. 11. W eka Manual. Available from: http://www.ittc. ;2014 Jul
[16] Witten, I.H., Frank, E.: “Data Mining: Practical Machine Learning Tools and Techniques,” 2nd edn. Morgan Kaufmann, San Francisco (2005).
[17] Tavallaee M.E, Bagheri W. Lu and Ghorbani A. “A Detailed Analysis of the KDD CUP 99 Data Set”, Proceedings of the Second IEEE Symposium on Computational Intelligence for Security and Defense Applications (CISDA), pp. 53-58. (2009),
[18] Xu, X.: “Adaptive Intrusion Detection Based on Machine Learning: Feature Extraction, Classifier Construction and Sequential Pattern Prediction.” International Journal of Web Services Practices 2(1-2), 49–58 (2006).
[19] Li, Y., Guo, L.: “An Active Learning Based TCMKNN Algorithm for Supervised Malicious Network node detection”. In: 26th Computers & Security pp. 459–467 (October 2007)
[20] “Nsl-KDD data set for network-based intrusion detection systems.” Available on: .
[21] Panda M. and Patra M.R (2008), “A Comparative study of Data Mining Algorithms for Network Intrusion Detection”, Proceedings of the 1st Conference on Emerging Trends in Engineering and Technology, pp. 504-507, IEEE Computer Society, USA.
[22] Amor N.B, Benferhat S. and Elouedi Z (2004), “Naïve Bayes vs. Decision Trees in Intrusion Detection Systems”, Proceedings of 2004, ACM Symposium on Applied Computing, pp. 420-424.
[23] G.MeeraGandhi, Kumaravel Appavoo, S.K.Srivatsa,” Effective Network Intrusion Detection using Classifiers Decision Trees and Decision rules”, Int. J. Advanced Networking and Applications Volume: 02, Issue: 03, Pages: 686-692 (2010).
[24] N. Shrivastva, A. Majumder and R. Rastogi, “Mining (Social) Network Graphs to Detect Random Link Attacks,” Proceedings of the IEEE 24th International Conference on Data Engineering, pp. 486-495, 2008.
[25] J. Karamon, Y. Matsuo and M. Ishizuka, “Generating useful networkbased features for analyzing social networks,” Proceedings of the 23rd national conference on Artificial intelligence, Vol. 2 (AAAI'08), pp. 1162-1168, 2008.
[26] M. Maia, J. Almeida, and V. Almeida, “Identifying user behavior in online social networks,” Proceedings of the 1st Workshop on Social Network Systems of ACM, pp. 1-6, 2008.
[27] M. Lahiri and T.Y. Berger-Wolf, “Mining Periodic Behaviour in Dynamic Social Networks,” Proceedings of the Eight IEEE Conference on data mining, pp. 373-382, 2008.
[28] Markines, C. Cattuto, and F. Menczer, “Social spam detection,” Proceedings of the 5th International Workshop on Adversarial Information Retrieval on the Web, pp. 41-48, 2009.
[29] Q. Wang, B. Liang, W. Shi, Z. Liang and W. Sun, “Detecting Spam Comments with Malicious Users’ Behavioral Characteristics,” Proceedings of Information Theory and Information Security (ICITIS), IEEE, pp. 563-567, 2010.
[30] Z. Halim, M. M. Gul, N. Hassan, R. Baig, S. Rehman and F. Naz, “Malicious Users' Circle Detection in Social Network Based on SpatioTemporal Co-Occurrence,” Computer Networks and Information Technology, IEEE, pp. 35-39, 2011.
[31] J. Cameron, C. Leung and S. Tanbeer, “Finding Strong Groups among Friends in Social Networks,” 9th International Conference on on Dependable, Autonomic and Secure Computing, IEEE, pp. 824-831, 2011.
[32] S. Al-Oufi, H. Kim and A. E. Saddik, “Controlling Privacy with Trustaware Link Prediction in Online Social Netwroks,” Proceedings of the 3rd International Conference on Internet Multimedia Computing and Service, ACM, pp. 86-89, 2011.
[33] D. Prakash and S. Surendran, “Detection and Analysis of Hidden Activities in Social Networks,” International Journal of Computer Applications, pp. 34-38, 2013.
[34] D. M. Freeman, “Using Naïve Bayes to Detect Spammy Names in Social Networks,” Proceedings of the ACM Workshop on Artifical Intelligence and Security, pp. 3-12, 2013.
[35] S.Y. Bhatt, M. Abulasih, “Community-Based Features for Identifying Spammers in Online Social Networks,” Proceedings of the International Conference on Advances in Social Networks Analysis and Mining, ACM, pp. 100-107, 2013.
[36] P. Dev, K. Singh and S. Dhawan, “Hidden Relationships for Analysing Users’ Behaviour in Heterogeneous Social Networks,” Proceedings of the 2nd National Conference on Converging Technologies Beyond 2020, pp. 297-300, 2014.
[37] H. Yin, B. Cui, L. Chen, Z. Hu and Z. Huang, “A Temporal Contextaware Model for User Behavior Modeling in Social Media Systems,” Proceedings of the International Conference on Management of Data, ACM, pp. 1543-1554, 2014.
[38] B. Viswanath, M. A. Bashir, M. Crovella, S. Guha, K. P. Gummadi, B. Krishnamurthy and A. Mislove, “Towards Detecting Anomalous User Behavior in Online Social Networks,” Proceedings of the 23rd USENIX Security, 2014.
[40] M. Jiang, P. Cui, A. Beutel, C. faloutsos and S. Yang, “Detecting Suspecious Following Behavior in Multimillion-Node Social Networks,” Proceedings of the 23rd International Conference on World Wide Web Companion, ACM, pp. 305-306, 2014.
[41] M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann and I. H. Witten, “The WEKA Data Mining Software: An Update,” SIGKDD Explorations, Vol. 11(1), pp. 10-18, 2009.
[42] B. Viswanath, A. Mislove, M. Cha, and K. P. Gummadi, “On the evolution of user interaction in Facebook,” Workshop on Online Social Networks, pp. 37–42, 2009.
[43] L. Backstrom, D. Huttenlocher, J. Kleinberg and X. Lan, “Group Formation in Large Social Networks: Membership, Growth, and Evolution.” Proceedings of the 12th International Conference on Knowledge Discovery and Data Mining, ACM, pp. 44-54, 2006.
[44] J. Leskovec, K. Lang, A. Dasgupta, M. Mahoney, “Community Structure in Large Networks: Natural Cluster Sizes and the Absence of Large Well-Defined Clusters,” Proceedings of the Internet Mathematics, Vol 6(1), pp. 29-123, 2009.

Classification Algorithms; Intrusion Detection System; Meta Classifier; Decision Trees; Machine Learning; Data Mining; WEKA.