Network Intrusion Detection Using Thermal Exchange Optimization and Seagull Optimization Algorithm

Document Type : Original Article

Authors

1 Assistant Professor, Department of Computer Engineering, Payame Noor University, Tehran, Iran.

2 PhD of Information Technology Management, Environment Protection Agency, Tehran, Iran.

10.48301/kssa.2023.389398.2481

Abstract

The increasing growth of computers and the internet has provided a new and widely used platform for providing network services. This has significantly increased the provision of administrative, social, financial, educational and recreational services on the network, particularly the internet. The expansion of the use of Internet applications creates an opportunity to abuse the network and its information for criminal purposes. Based on this, intrusion into the network and unauthorized access to information have become the main concerns of network users as well as network managers. Intrusion detection systems include a set of tools and mechanisms for monitoring computer systems and network traffic. Various methods are used for intrusion detection, such as statistical techniques, cognitive-based methods, and machine learning methods. In the present research, a method for intrusion detection using machine learning algorithms was reviewed and proposed. The proposed model is a multi-class method that, in addition to intrusion detection, also determines the type of attack. This method is a hybrid model in which the combination of the Seagull optimization algorithm, thermal exchange optimization algorithms and random forest algorithm are used. CICIDS-2017 dataset was used for analysis in this research. The proposed method was compared with several different algorithms and the accuracy value of the proposed method was equal to 98.8, which is higher than that of many machine learning methods.

Keywords

Main Subjects

References

[1] Kappagantu, M., Villamor, D. E. V., Bullock, J. M., & Eastwell, K. C. (2017). A rapid isothermal assay for the detection of Hop stunt viroid in hop plants (Humulus lupulus), and its application in disease surveys. Journal of Virological Methods, 245, 81-85. https://d oi.org/10.1016/j.jviromet.2017.04.002
[2] Dhiman, G., & Kumar, V. (2019). Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems. Knowledge-Based Systems, 165, 169-196. https://doi.org/10.1016/j.knosys.2018.11.024
[3] Kaveh, A., & Dadras, A. (2017). A novel meta-heuristic optimization algorithm: Thermal exchange optimization. Advances in Engineering Software, 110, 69-84. https://doi.o rg/10.1016/j.advengsoft.2017.03.014
[4] Mohammadi, S., Mirvaziri, H., Ghazizadeh Ahsaee, M., & Karimipour, H. (2019). Cyber intrusion detection by combined feature selection algorithm. Journal of Information Security and Applications, 44, 80-88. https://doi.org/10.1016/j.jisa.2018.11.007
[5] Rios, A. L. G., Li, Z., Bekshentayeva, K., & Trajković, L. (2020, October 12-14). Detection of Denial of Service Attacks in Communication Networks. 2020 Institute of Electrical and Electronics Engineers International Symposium on Circuits and Systems Seville, Spain. https://doi.org/10.1109/ISCAS45731.2020.9180445
[6] Bedi, P., Gupta, N., & Jindal, V. (2020). Siam-IDS: Handling class imbalance problem in Intrusion Detection Systems using Siamese Neural Network. Procedia Computer Science, 171, 780-789. https://doi.org/10.1016/j.procs.2020.04.085
[7] Marir, N., Wang, H., Feng, G., Li, B., & Jia, M. (2018). Distributed Abnormal Behavior Detection Approach Based on Deep Belief Network and Ensemble SVM Using Spark. Institute of Electrical and Electronics Engineers Access, 6, 59657-59671. https://doi .org/10.1109/ACCESS.2018.2875045
[8] Kurochkin, I., & Volkov, S. (2020, September 6-13). Using GRU based deep neural network for intrusion detection in software-defined networks. Institute of Physics Conference Series: Materials Science and Engineering 2020:  XIII International Conference on Applied Mathematics and Mechanics in the Aerospace Industry, Alushta, Russia. ht tps://doi.org/10.1088/1757-899X/927/1/012035
[9] Mulyanto, M., Faisal, M., Prakosa, S. W., & Leu, J-S. (2021). Effectiveness of Focal Loss for Minority Classification in Network Intrusion Detection Systems. Symmetry, 13(1), 4. https://doi.org/10.3390/sym13010004
[10] Abdolhosseini, M., Abdollahi, R., & Rajaee, M. (2021). Designing of PIλDδ controller for PMBLDC motor using metaheuristic algorithms. Karafan Quarterly Scientific Journal, 17(4), 149-165. https://doi.org/10.48301/kssa.2021.128401
[11] Tama, B. A., & Lim, S. (2021). A Stacking-Based Deep Neural Network Approach for Effective Network Anomaly Detection. Computers Materials & Continua, 66(2), 2217 - 2227. https://doi.org/10.32604/cmc.2020.012432
[12] Vinayakumar, R., Alazab, M., Soman, K. P., Poornachandran, P., Al-Nemrat, A., & Venkatraman, S. (2019). Deep Learning Approach for Intelligent Intrusion Detection System. Institute of Electrical and Electronics Engineers Access, 7, 41525-41550. https://doi.org/10.1109/ACCESS.2019.2895334
[13] Sharafaldin, I., Lashkari, A. H., & Ghorbani, A. A. (2018, January 22-24). Toward generating a new intrusion detection dataset and intrusion traffic characterization. Proceedings of the 4th International Conference on Information Systems Security and Privacy, Funchal, Madeira, Portugal. https://doi.org/10.5220/0006639801080116
[14] Singh Panwar, S., Raiwani, Y., & Panwar, L. S. (2019, March 15). Evaluation of network intrusion detection with features selection and machine learning algorithms on CICIDS-2017 dataset. International Conference on Advances in Engineering Science Management & Technology 2019 Uttaranchal University, Dehradun, India. http://dx.doi.org/10.2 139/ssrn.3394103
[15] Gao, D., Zhang, Y-X., & Zhao, Y-H. (2009). Random forest algorithm for classification of multiwavelength data. Research in Astronomy and Astrophysics, 9(2), 220-226. https://doi.org/10.1088/1674-4527/9/2/011
[16] Bhavani, T. T., Rao, M. K., & Reddy, A. M. (2020). Network Intrusion Detection System Using Random Forest and Decision Tree Machine Learning Techniques. In A. K. Luhach, J. A. Kosa, R. C. Poonia, X.-Z. Gao, & D. Singh (Eds.), First International Conference on Sustainable Technologies for Computational Intelligence. Springer Singapore. h ttps://doi.org/10.1007/978-981-15-0029-9_50
[17] Mirjalili, S. (2016). Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Computing and Applications, 27(4), 1053-1073. https://doi.org/10.1007/s00521-015-1920-1
[18] Rohart, F., Gautier, B., Singh, A., & Lê Cao, K-A. (2017). mixOmics: An R package for ‘omics feature selection and multiple data integration. PLoS computational biology, 13(11), e1005752. https://doi.org/10.1371/journal.pcbi.1005752
[19] Rashno, A., Nazari, B., Sadri, S., & Saraee, M. (2017). Effective pixel classification of Mars images based on ant colony optimization feature selection and extreme learning machine. Neurocomputing, 226, 66-79. https://doi.org/10.1016/j.neucom.2016.11.030
[20] Joshi, R. (2016). Accuracy, Precision, Recall & F1 Score: interpretation of performance measures.
Karafan Quarterly Scientific Journal
Volume 20, Issue 3
Engineering
December 2024
Pages 509-529

Files

History

  • Receive Date: 01 April 2023
  • Revise Date: 01 July 2023
  • Accept Date: 05 September 2023

Share

How to cite

Statistics