Abstract
Lazy classifiers are the local learner, where the model is built only when the test instances are present at hand. In this paper, five lazy learners are explored using ISCXIDS2012 and CICNTTor2016 datasets provided by the Canadian Institute of Cybersecurity. In order to explore these classifiers, 11 performance measures are considered for each dataset separately. The result shows that KStar classifier suitable for ISCXIDS2012 and both IB1 and IBkLG classifiers works well in the CICNTTor2017 dataset. Therefore, these classifiers can be used as an adaptive intrusion detection engine to detect possible intrusions by preserving inherent late learning property of lazy classifiers.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
E.K. Garcia, S. Feldman, M.R. Gupta, S. Srivastava, Completely lazy learning. IEEE Trans. Knowl. Data Eng. 22(9), 1274–1285 (2010)
P. Tiwari, H. Dao, Nguyen GN: performance evaluation of lazy, decision tree classifier and multilayer perceptron on traffic accident analysis. Informatica 41(1), 13 (2017)
D. Aha, D. Kibler, Instance-based learning algorithms. Mach. Learn. 6, 37–66 (1991)
E. Frank, Fully supervised training of Gaussian radial basis function networks in WEKA. Department of Computer Science, University of Waikato (2014)
J.G. Cleary, L.E. Trigg, K*, an instance-based learner using an entropic distance measure, in 12th International Conference on Machine Learning, pp. 108–114 (1995)
E. Frank, M. Hall, B. Pfahringer, Locally weighted Naive Bayes, in 19th Conference in Uncertainty in Artificial Intelligence, pp. 249–256 (2003)
C. Atkeson, A. Moore, S. Schaal, Locally weighted learning. AI Review (1996)
A. Shiravi, H. Shiravi, M. Tavallaee, A. Ghorbani, Toward developing a systematic approach to generate benchmark datasets for intrusion detection. Comput. Secur. 357–374 (2012). https://doi.org/10.1016/j.cose.2011.12.012
A.H. Lashkari, G. Draper-Gil, M.S. Mamun, A. Ghorbani, Characterization of Tor traffic using time based features, in Proceedings of the 3rd International Conference on Information Systems Security and Privacy, ICISSP, vol. 1, pp. 253–262 (2017). ISBN 978-989-758-209-7. https://doi.org/10.5220/0006105602530262
S. Vijayarani, M. Muthulakshmi, Comparative analysis of bayes and lazy classification algorithms. Int. J. Adv. Res. Comput. Commun. Eng. 2(8), 3118–3124 (2013)
V. Kumar, R. Zinovyev, A. Verma, P. Tiwari, Performance evaluation of lazy and decision tree classifier: a data mining approach for global celebrity’s death analysis, in 2018 International Conference on Research in Intelligent and Computing in Engineering (RICE) 22 Aug 2018, pp. 1–6. IEEE
H. Mukherjee, S.M. Obaidullah, K.C. Santosh et al., Int. J. Mach. Learn. Cyber. (2019). https://doi.org/10.1007/s13042-019-00928-3
M.E. Rimer, T.R. Martinez, D.R. Wilson, Improving speech recognition learning through lazy training. in Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN’02 (Cat. No. 02CH37290), vol. 3 (IEEE, 2002), pp. 2568–2573
L. Pengfei, T. Wulei, Apply ensemble of lazy learners to biomedical data mining. in Intelligent Computing and Information Science, ICICIS 2011 ed. by R. Chen. Communications in Computer and Information Science, vol. 134 (Springer, Berlin, 2011)
J. Singh, H. Kaur, Plant disease detection based on region-based segmentation and KNN classifier, in Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB), ISMAC 2018, ed. by D. Pandian, X. Fernando, Z. Baig, F. Shi. Lecture Notes in Computational Vision and Biomechanics, vol. 30 (Springer, Cham, 2019)
P. Mulak, D.P. Gaikwad, N.R. Talhar, K-Nearest Neighbor and boundary cutting algorithm for intrusion detection system. In: Information Systems Design and Intelligent Applications, ed. by S. Satapathy, J. Mandal, S. Udgata, V. Bhateja. Advances in Intelligent Systems and Computing, vol. 434 (Springer, New Delhi, 2016)
D. Dave, S. Vashishtha, Efficient intrusion detection with KNN classification and DS theory, in Proceedings of All India Seminar on Biomedical Engineering 2012 (AISOBE 2012) ed. by V. Kumar, M. Bhatele. Lecture Notes in Bioengineering (Springer, New Delhi, 2013)
A. Chellam, L. Ramanathan, S. Ramani, Intrusion detection in computer networks using lazy learning algorithm. Proc. Comput. Sci. 31(132), 928–936 (2018)
J. Wills, Algorithms every data scientist should know: reservoir sampling, 23 April 2013. Source: https://blog.cloudera.com/blog/2013/04/hadoop-stratified-randosampling-algorithm
R. Panigrahi, S. Borah, Classification and Analysis of Facebook Metrics Dataset Using Supervised Classifiers, Social Network Analytics (Academic Press, Elsevier, 2019). ISBN: 978-0-12-815458-8, pp. 1–19
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Panigrahi, R., Borah, S. (2020). A Statistical Analysis of Lazy Classifiers Using Canadian Institute of Cybersecurity Datasets. In: Borah, S., Emilia Balas, V., Polkowski, Z. (eds) Advances in Data Science and Management. Lecture Notes on Data Engineering and Communications Technologies, vol 37. Springer, Singapore. https://doi.org/10.1007/978-981-15-0978-0_21
Download citation
DOI: https://doi.org/10.1007/978-981-15-0978-0_21
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-0977-3
Online ISBN: 978-981-15-0978-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)