A Swarm- Optimized Hybrid Approach to Feature Selection in IDS
DOI:
https://doi.org/10.29304/jqcsm.2025.17.42565Keywords:
Artificial Bee Colony; Genetic Algorithm; Ensemble Learning; Anomaly Detection.Abstract
The network intrusion detection (IDS) strategies are important to securing our systems and networks from unauthorized behaviors. An IDS routinely deals with large amounts of data transmission that contain non-informative and duplicate features, which implication the performance of the machine learnind model negatively. In this paper, we proposed on hybrid two optimization techniques Artificial Bee Colony (ABC) and Genetic Algorithm (GA) to feature selected data. we used Random Forest (RF) and XGBoost classifiers to evaluate how well they perform with the reduced features. Experiments are conducted on three datasets: NSL-KDD, UNSW-NB15, and CIC-IDS2017. Experimental results show that the ABC-GA algorithm reduces the number of features (up to 70-88%) while maintaining detection accuracy, with accuracy reaching 97% in NSL-KDD, 92% in UNSW-NB15, and 94% in CIC-IDS2017.
Downloads
References
Z. Halim and S. Shafique, “An effective genetic algorithm-based feature selection method for intrusion detection systems,” Computers & Security, 2021.
P. Nimbalkar and S. Nimbalkar, “Feature selection for intrusion detection system in Internet-of-Things,” ICT Express (Elsevier), 2021. Available: ScienceDirect
O. Almomani, “A Feature Selection Model for Network Intrusion Detection System,” Symmetry, 2020.
M.-T. Nguyen, H. Fang, et al., “Genetic convolutional neural network for intrusion detection,” Future Generation Computer Systems (Elsevier), 2020.
Y. Yin, J. Jang-Jaccard, et al., “IGRF-RFE: A hybrid feature selection method for MLP-based network intrusion detection on UNSW-NB15,” Journal of Big Data, 2023 (open PDF).
A. H. Farooqi, S. A. Chaudhry, et al., “Enhancing Network Intrusion Detection Using an Ensemble Voting Classifier (DRX),” Sensors, 2023.
Y. Alotaibi and M. Mehmood, “Ensemble-Learning Framework for Intrusion Detection to Improve Efficiency,” Sensors, 2023.
H. Yu, F. Wang, et al., “A feature selection algorithm for intrusion detection system based on enhanced heuristic optimization,” Expert Systems with Applications, 2024.
S. A. Ajagbe, A. Adeniyi, et al., “A Comparison Study of ML Models Using Feature Selection on UNSW-NB15,” SN Computer Science, 2024.
G. Balhareth, A. Alshahrani, et al., “Optimized Intrusion Detection for IoMT Networks with Tree-based Classifiers and Filter FS,” Sensors, 2024.
J. Bo, Y. Tong, et al., “Boosting Few-Shot Network Intrusion Detection with Adaptive Feature Fusion,” Electronics, 2024.
E. M. Maseno, Y. He, et al., “Hybrid wrapper feature selection method based on genetic algorithm for IDS,” Journal of Big Data, 2024 (open PDF).
M. Wang, X. Jiang, et al., “Learn-IDS: Bridging Gaps between Datasets and Models for Network IDS,” Electronics, 2024.
G. Nassreddine and A. Baz, “Ensemble Learning for Network Intrusion Detection Based on Correlation and XGBoost-Embedded FS,” Computers, 2025.
E. Emirmahmutoğlu and A. Genç, “A feature selection-driven ML framework for anomaly-based attack detection,” Peer-to-Peer Networking and Applications, 2025.
X.-Y. Gong, Z.-H. Zhou, et al., “Feature selection method for network intrusion based on hybrid metaheuristic (HMDOA),” Computers & Security, 2025.
Y. Gao, J. Li, et al., “IR-IDS: A network intrusion detection method based on causal feature selection and robust anomaly classification,” Computers & Security, 2025.
J. Maldonado, R. Sosa, et al., “An evolutionary wrapper to support intrusion detection with adaptive model selection,” Computers & Security, 2025.
M. Alharthi, F. Medjek, and D. Djenouri, “Ensemble Learning Approaches for Multi-Class IDS for the IoV: A Comprehensive Survey,” Future Internet, 2025.
N. Islam, F. Farhin, I. Sultana, M. S. Kaiser, M. S. Rahman, M. Mahmud, and G. H. Cho, “Towards machine learning based intrusion detection in IoT networks,” Computers, Materials & Continua, vol. 69, no. 2, pp. 1983–1997, 2021.
A. M. Banaamah and I. Ahmad, “Intrusion detection in IoT using deep learning,” Sensors, vol. 22, no. 21, p. 8417, 2022, doi: 10.3390/s22218417.
E. Altulaihan, M. A. Almaiah, and A. Aljughaiman, “Cybersecurity threats, countermeasures and mitigation techniques on the IoT: Future research directions,” Electronics, vol. 11, no. 11, p. 3330, 2022, doi: 10.3390/electronics11203330.
S. A. Arhore, Intrusion Detection in IoT Systems Using Machine Learning, Doctoral dissertation, National College of Ireland, Dublin, 2022.
M. Baich, T. Hamim, N. Sael, and Y. Chemlal, “Machine learning for IoT based networks intrusion detection: A comparative study,” Procedia Computer Science, vol. 215, pp. 742–751, 2022.
B. Aslahi-Shahri, R. Rahmani, M. Chizari, A. Maralani, M. Eslami, M. J. Golkar, and A. Ebrahimi, “A hybrid method consisting of GA and SVM for intrusion detection system,” Neural Computing and Applications, vol. 27, no. 6, pp. 1669–1676, 2016.
H. Alazzam, A. Sharieh, and K. E. Sabri, “A feature selection algorithm for intrusion detection system based on pigeon inspired optimizer,” Expert Systems with Applications, vol. 148, Art. no. 113249, 2020.
C. Khammassi and S. Krichen, “A GA-LR wrapper approach for feature selection in network intrusion detection,” Computers & Security, vol. 70, pp. 255–277, 2017.
M. Tavallaee, E. Bagheri, W. Lu, and A. A. Ghorbani, “A detailed analysis of the KDD CUP 99 data set,” in Proc. IEEE Symp. Computational Intelligence for Security and Defense Applications (CISDA), Ottawa, ON, Canada, 2009, pp. 1–6. doi: 10.1109/CISDA.2009.5356528.
N. Moustafa and J. Slay, “UNSW-NB15: A comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set),” in Proc. 2015 Military Communications and Information Systems Conf. (MilCIS), Canberra, Australia, 2015, pp. 1–6. doi: 10.1109/MilCIS.2015.7348942.
I. Sharafaldin, A. H. Lashkari, and A. A. Ghorbani, “Toward generating a new intrusion detection dataset and intrusion traffic characterization,” in Proc. 4th Int. Conf. Information Systems Security and Privacy (ICISSP), Funchal, Portugal, 2018, pp. 108–116. doi: 10.5220/0006639801080116.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Maan Y Anad Alsaleem
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
