A Hybrid approach for Intrusion Detection using K-Nearest Neighbor and Artificial Neural Network
Main Article Content
Abstract
Downloads
Article Details
COPYRIGHT
Submission of a manuscript implies: that the work described has not been published before, that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication, the authors agree to automatic transfer of the copyright to the publisher.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
- The journal allows the author(s) to retain publishing rights without restrictions.
- The journal allows the author(s) to hold the copyright without restrictions.
References
J. K. Chahal and A. Kaur, “A hybrid approach based on classification and clustering for intrusion detection system,†Int. J. Math. Sci. Comput., vol. 4, no. November 2016, pp. 34–40, 2016.
J. J. Davis and A. J. Clark, “Data preprocessing for anomaly based network intrusion detection: A review,†Comput. Secur., vol. 30, no. 6–7, pp. 353–375, 2011.
S. Choudhury and A. Bhowal, “Comparative analysis of machine learning algorithms along with classifiers for network intrusion detection,†in 2015 International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials, ICSTM 2015 - Proceedings, 2015, no. May, pp. 89–95.
W. Wang, X. Guan, and X. Zhang, “Processing of massive audit data streams for real-time anomaly intrusion detection,†Comput. Commun., vol. 31, no. 1, pp. 58–72, 2008.
A. Özgür and H. Erdem, “The impact of using large training data set KDD99 on classification accuracy,†PeerJ, vol. 5, no. March, 2017.
T. Mehmood and H. B. Rais, “Machine learning algorithms in context of intrusion detection,†in 3rd International Conference on Computer and Information Sciences (ICCOINS), 2016, pp. 369–373.
V. Kshirsagar and M. S. Joshi, “Rule based classifier models for intrusion detection system,†Int. J. Comput. Sci. Inf. Technol., vol. 7, no. 1, pp. 367–370, 2016.
T. R. Devi and S. Badugu, “A Review on Network intrusion detection systems using machine learning,†in International Conference on Emerging Trends in Engineering, 2020, pp. 598–607.
R. Sommer and V. Paxson, “Outside the closed world: On using machine learning for network intrusion detection,†in Proceedings - IEEE Symposium on Security and Privacy, 2010, pp. 305–316.
G. M. Gandhi, K. Appavoo, and S. K. Srivatsa, “Effective network intrusion detection using classifiers decision trees and decision rules,†Int. J. Adv. Netw. Appl., vol. 2, no. 3, pp. 686–692, 2010.
P. Ghosh, C. Debnath, D. Metia, and D. R. Dutta, “An efficient hybrid multilevel intrusion detection system in cloud environment,†IOSR J. Comput. Eng., vol. 16, no. 4, pp. 16–26, 2014.
S. Lakhina, S. Joseph, and B. Verma, “Feature reduction using principal component analysis for effective anomaly–based intrusion detection on NSL-KDD,†Int. J. Eng. Sci. Technol., vol. 2, no. 6, pp. 1790–1799, 2010.
G. Kim, S. Lee, and S. Kim, “A novel hybrid intrusion detection method integrating anomaly detection with misuse detection,†Expert Syst. Appl., vol. 41, no. 4 PART 2, pp. 1690–1700, 2014.
B. M. Aslahi-Shahri et al., “A hybrid method consisting of GA and SVM for intrusion detection system,†Neural Comput. Appl., vol. 27, no. 6, pp. 1669–1676, 2016.
R. M. Elbasiony, E. A. Sallam, T. E. Eltobely, and M. M. Fahmy, “A hybrid network intrusion detection framework based on random forests and weighted k-means,†Ain Shams Eng. J., vol. 4, no. 4, pp. 753–762, 2013.
K. Potdar, T. S., and C. D., “A comparative study of categorical variable encoding techniques for neural network classifiers,†Int. J. Comput. Appl., vol. 175, no. 4, pp. 7–9, 2017.
O. I. Aladesote, A. Olutola, and O. Olayemi, “Feature or attribute extraction for intrusion detection system using gain ratio and Principal Component Analysis (PCA),†Commun. Appl. Electron., vol. 4, no. 3, pp. 1–4, 2016.
K. K. Vasan and B. Surendiran, “Dimensionality reduction using Principal Component Analysis for network intrusion detection,†Perspect. Sci., vol. 8, no. September, pp. 510–512, 2016.
I. S. Atawodi, “A machine learning approach to network intrusion detection system using K Nearest Neighbor and Random Forest,†Masters Thesis, 2019.
K. Chumachenko, “machine learning methods for malware detection and classification,†Proc. 21st Pan-Hellenic Conf. Informatics - PCI 2017, p. 93, 2017.
F. Haddadi, S. Khanchi, M. Shetabi, and V. Derhami, “Intrusion detection and attack classification using feed-forward neural network,†in 2nd International Conference on Computer and Network Technology, ICCNT 2010, 2010, pp. 262–266. doi: 10.1109/ICCNT.2010.28
P. Sibi, S. Allwyn Jones, and P. Siddarth, “Analysis of different activation functions using back propagation neural networks,†J. Theor. Appl. Inf. Technol., vol. 47, no. 3, pp. 1344–1348, 2013.