Network Intrusion Detection Using Optimal Perception with Cuckoo Algorithm
DOI:
https://doi.org/10.31185/wjps.326Keywords:
cuckoo algorithm, perceptronAbstract
ABSTRACT
To safeguard computer networks from intruders, intrusion detection systems have been created. These systems operate in conjunction with firewalls and other security measures to guarantee the safety and efficiency of the computer system. An intrusion detection system is a tool designed to detect and pinpoint attacks and vulnerabilities within a network or computer system. It subsequently notifies the system administrator of them. The primary challenge with intrusion detection systems is enhancing their speed and precision in detecting intruders. This article explores a novel technique for identifying attempts to infiltrate computer systems. The system utilizes a hybrid approach involving the cuckoo algorithm and perceptron neural network. This novel approach can detect intrusion data more accurately than previous methods and enhance the detection rate by over 1%. The system utilizes the cuckoo method to choose a subset of characteristics, which are then analyzed based on the frequency of various attribute types in intrusive and normal data using an optimum perceptron. The system has been evaluated and the implementation has yielded a detection accuracy of 89.8%, representing a substantial enhancement compared to earlier approaches.
References
Scarfone, Karen A., and Peter M. Mell. Guide to Intrusion Detection and Prevention Systems (IDPS)| NIST. No. Special Publication (NIST SP)-800-94. 2007.
Sekar, Ramasubramanian, Ajay Gupta, James Frullo, Tushar Shanbhag, Abhishek Tiwari, Henglin Yang, and Sheng Zhou. ”Specification-based anomaly detection: a new approach for detecting network intrusions.” In Proceedings of the 9th ACM conference on Computer and communications security, pp. 265-274. ACM, 2002.
Demuth, Howard B., Mark H. Beale, Orlando De Jess, and Martin T. Hagan. Neural network design. Martin Hagan, 2014.
Chiba, Z., Abghour, N., Moussaid, K., El Omri, A., & Rida, M. (2018). A novel architecture combined with optimal parameters for back propagation neural networks applied to anomaly network intrusion detection. Computers & Security, 75: p. 36‐58.
Chellam, A., Ramanathan, L., & Ramani, S. (2018). Intrusion detection in computer networks using lazy learning algorithm. Procedia computer science, 132: p. 928‐936 .
Wang, C.-R., Xu, R.-F., Lee, S.-J., & Lee, C.-H. (2018). Network intrusion detection using equality constrained-optimization-based extreme learning machines. Knowledge-Based Systems, 147, 68-80.
Ashfaq, R. A. R., Wang, X.-Z., Huang, J. Z., Abbas, H., & He, Y.-L. (2017). Fuzziness based semi-supervised learning approach for intrusion detection system. Information Sciences, 378: p. 484‐497 .
Vishwakarma, S., Sharma, V., & Tiwari, A. (2017). An intrusion detection system using KNN-ACO algorithm. Int. J. Comput. Appl., 171(10), 18-23 .
Amfo, J. K., & Hayfron-Acquah, J. B. (2018). Modeling of Hybrid Intrusion Detection System in Internet of Things using Support Vector Machine and Decision Tree. International Journal of Computer Applications, 181(15): p. 45‐52.
Li, W., Özcan, E., & John, R. (2017). Multi-objective evolutionary algorithms and hyper-heuristiالگوریتم فاخته for wind farm layout optimisation. Renewable Energy, 1 :p. 473‐482 .
F. Valeur, G. Vigna, C. Kruegel, R. Kemmerer, "Comprehensive approach to intrusion detection alert correlation", IEEE transaction on dependable and secure computing, 2008, pp. 146-169.
S.J. Horng, M.Y. Su," A novel intrusion detection system based on hierarchical clustering and support vector machines", Expert systems with applications 38 ,2011, pp. 306–313.
T. Chyssler, S.N. Tehrani, "Alarm reduction and correlation in defence of IP networks", Proceedings of the 13th International workshops on enabling technologies, IEEE computer society, 2008, pp. 229-234.
Y. Gong, S. Mabo, C. Chen, "Intrusion detection system combining misuse detection and anomaly detection using genetic network programming", ICROS-SICE international joint conference, 2009, pp. 3463-3467.
Y. Li, J .Xia, S. Zhang, " An efficient intrusion detection system based on support vector machines and gradually feature removal method", Expert systems with applications 39, 2012 pp. 424–430.
Y.P. Zhou, " Hybrid model based on artificial immune system and PCA neural networks for intrusion detection", IEEE APCIP(Asian-pacific conference of information processing), 2009, pp. 21-24.
Almomani O. A feature selection model for network intrusion detection system based on pso, gwo, ffa and ga algorithms. Symmetry 2020;12(6):1046.
Alazab M, Khurma RA, Awajan A, Camacho D. A new intrusion detection system based on moth–flame optimizer algorithm. Expert Syst Appl 2022;210:118439.
Alzubi QM, Anbar M, Sanjalawe Y, Al-Betar MA, Abdullah R. Intrusion detection system based on hybridizing a modified binary grey wolf optimization and particle swarm optimization. Expert Syst Appl 2022;204:117597.
Moutaz Alazab, Ruba Abu Khurma, Pedro A. Castillo, Bilal Abu-Salih, Alejandro Martín, David Camacho,An effective networks intrusion detection approach based on hybrid Harris Hawks and multi-layer perceptron,Egyptian Informatics Journal,Volume 25,2024,100423,ISSN 1110-8665,https://doi.org/10.1016/j.eij.2023.100423.
Yang XS, Algorithms NI. Luniver Press. Beckington, UK; 242-6, 2008
Hagan, M. T., H. B. Demuth, M. H. Beale, and O. De Jesús. ”Neural Network Design, 2nd Edtion.” Stillwater, Oklahoma. Oklahoma State University (2014).
Gandomi AH, Yang X-S, Alavi AH (2013) Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng Comput 29(1):17–35
Subba, Basant, Santosh Biswas, and Sushanta Karmakar. "Intrusion detection in Mobile Ad-hoc Networks: Bayesian game formulation." Engineering Science and Technology, an International Journal 19, no. 2 (2016): 782-799.
Bul'ajoul, Waleed, Anne James, and Mandeep Pannu. "Improving network intrusion detection system performance through quality of service configuration and parallel technology." Journal of Computer and System Sciences 81, no. 6 (2015): 981-999.
Elhag, Salma, Alberto Fernández, Abdullah Bawakid, Saleh Alshomrani, and Francisco Herrera. "On the combination of genetic fuzzy systems and pairwise learning for improving detection rates on Intrusion Detection Systems." Expert Systems with Applications 42, no. 1 (2015): 193-202.
Hajimirzaei, B., & Navimipour, N. J. (2019). Intrusion detection for cloud computing using neural networks and artificial bee colony optimization algorithm. ICT Express, 5(1): p. 56‐59
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Hameed Lafta
This work is licensed under a Creative Commons Attribution 4.0 International License.
