Optimizing Intrusion Detection using Intelligent Feature Selection with Gray wolf-based FOX Algorithm
DOI:
https://doi.org/10.63841/iue21527Keywords:
Intrusion Detection, Feature Selection, Network Security, , Gray Wolf Optimization, FOX Algorithm, CybersecurityAbstract
Intrusion Detection Systems (IDS) are crucial in protecting computer networks against malicious activities. However, the performance of IDS can be improved by selecting the most relevant features from the vast amount of network traffic data. This article proposes an innovative approach to optimizing intrusion detection using intelligent feature selection with the FOX algorithm based on Grey Wolf optimization. In this study, intrusion detection is conducted using the KDDCup99 database. Then, processed features are selected. After preprocessing and preparing the dataset for data mining, they are fed into an MLP neural network. Each of the features and findings play a significant role in intrusion detection and prediction. In other words, not all features are equally valuable. Determining the value and role of each feature in intrusion detection is crucial. In this study, the value and role of each of these features are optimized and intrusion is identified by the Grey Wolf Optimization (GWO) algorithm. The proposed method's suitable accuracy compared to other classification algorithms used in this research such as Support Vector Machines and Decision Trees, demonstrates the efficiency and superiority of the proposed method.
References
Emary .E, Zawbaa .H.M., Zawbaa .A.E.(2016) Hassanien, Binary grey wolf optimization approaches for feature selection, Neurocomputing 172 . 371–381, https://doi. org/10.1016/j.neucom.2015.06.083.
Ghazal T M.(2022) Data Fusion-based machine learning architecture for intrusion detection. Computers, Materials & Continua, 70(2): 3399- 3413.
Gligor .V. D.,(2006) “A note on the denial-of-service problem.” in IEEE Symposium on Security and Privacy, p. 139-149.
Kumar, A., & Lee, S. (2024). Comparative analysis of ensemble learning methods for heart disease prediction. Journal of Medical Informatics, 35(2), 145-160.
Li, J., Zhang, L., & Wang, Y. (2024). Hybrid convolutional neural networks and traditional machine learning algorithms for heart disease diagnosis. Journal of Artificial Intelligence in Medicine, 12(3), 113-125.
Huang Y, Pullen JM,(2016) “Countering denial-of-service attacks using Congestion triggered packet sampling and filtering”, Presented at 10th International Conference on Computer Communications and Networks.
Islam N, Farhin F, Sultana I, et al.(2021) Towards machine learning based intrusion detection in IoT networks. Comput. Mater. Contin, , 69(2): 1801-1821.
Pourbahrami, S., Balafar, M. A., Khanli, L. M., & Kakarash, Z. A. (2020). A survey of neighborhood construction algorithms for clustering and classifying data points. Computer Science Review, 38, 100315.
Kareem .S.S, Mostafa .R.R, Hashim .F.A., El-Bakry., (2022) An effective feature selection model using hybrid metaheuristic algorithms for IoT intrusion detection, Sensors 22 (4) , https://doi.org/10.3390/s22041396.
Khan .S, Traore .I.,(2015) Queue-based analysis of DoS attacks, in: Proceeding of the 2015 IEEE Workshop on Information Assurance and Security, United States Mulitary Academy, West Point, NY, pp. 266–273.
Kumar A,. (2020). “Intrusion Detection using Feature Selection and Machine Learning Techniques." Expert Systems with Applications 47: 106-119.
Mohammed .H, Rashid .T.,(2023) FOX: a FOX-inspired optimization algorithm, Appl. Intell. 53 (1) 1030–1050, https://doi.org/10.1007/s10489-022- 03533-0.
Kakarash, Z. A., Mardukhia, F., & Moradi, P. (2023). Multi-label feature selection using density-based graph clustering and ant colony optimization. Journal of Computational Design and Engineering, 10(1), 122-138.
Mothukuri V, Khare P, Parizi R M, et al(2021). Federated-learning-based anomaly detection for iot security attacks. IEEE Internet of Things Journal, 9(4): 2545-2554
Safa .H, Chouman .M, Artail .H, Karam .M.,(2014) "A collaborative defense mechanism against SYN flooding attacks in IP networks", Journal of Network and Computer Applications, Volume 31, Pages 509-534.
SaiSindhuTheja .R , Shyam .G.K.,(2021) An efficient metaheuristic algorithm based feature selection and recurrent neural network for DoS attack detection in cloud computing environment, Appl. Soft Comput. 100 (Mar. 2021) 106997, https://doi.org/10.1016/j.asoc.2020.106997.
Liu, H., Wang, Q., & Zhou, F. (2024). Personalized machine learning models for heart disease diagnosis: The role of genetics and lifestyle factors. Journal of Medical Genetics and Informatics, 31(1), 25-36.
McDonald, J., Cooper, D., & Stevens, R. (2024). Generative adversarial networks for augmenting heart disease datasets. International Journal of Machine Learning in Medicine, 40(7), 1789-1802.
Patel, V., Gupta, R., & Singh, K. (2023). Addressing imbalanced datasets in heart disease prediction using oversampling techniques. Healthcare AI Journal, 29(4), 210-222.
Kakarash, Z. A., Ezat, H. S., Omar, S. A., & Ahmed, N. F. (2022). Time series forecasting based on support vector machine using particle swarm optimization. International Journal of Computing, 21(1), 76-88.
Waheed N, He X, Ikram M, et al.(2020) Security and privacy in IoT using machine learning and blockchain: Threats and countermeasures. ACM Computing Surveys (CSUR), 53(6): 1-37.
Kakarash, Z. A., Karim, S. H. T., Ahmed, N. F., & Omar, G. A. (2021). New topology control base on ant colony algorithm in optimization of wireless sensor network. Passer Journal of Basic and Applied Sciences, 3(2), 123-129.
Yang Wang , Chuang Lin , Quan-Lin Li, Yuguang Fang ,(2007)"A queueing analysis for the denial of service (DoS) attacks in computer network", Computer Networks 51 3564–3573.
Kakarash, Z. A., Karim, S. H. T., & Mohammadi, M. (2020). Fall detection using neural network based on internet of things streaming data. UHD Journal of Science and Technology, 4(2), 91-98.
Smith, T., Miller, H., & Thomas, P. (2023). Decision trees for heart disease prediction: A comparative study. International Journal of Health Informatics, 41(1), 56-67.
Tan, J., & Lee, H. (2023). Unsupervised learning for feature extraction in heart disease diagnosis. Journal of Data Mining in Healthcare, 11(2), 84-95.
Zhang, Y., Zhao, J., & Liu, B. (2022). Deep learning for heart disease diagnosis: Accuracy and interpretability challenges. Journal of Machine Learning in Healthcare, 18(5), 82-94.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Academic Journal of International University of Erbil
This work is licensed under a Creative Commons Attribution 4.0 International License.
