Volume 17 , Issue 2 , PP: 97-112, 2026 | Cite this article as | XML | Html | PDF | Full Length Article
Ahmad Salim 1 , Obaid Salim 2 , Omar Muthanna Khudhur 3 * , Shokhan M. Al-Barzinji 4 , Farah Maath Jasem 5
Doi: https://doi.org/10.54216/JCIM.170208
Intrusion detection has garnered significant attention as researchers strive to develop sophisticated models characterized by their high accuracy levels. However, the persistent challenge lies in creating reliable and effective intrusion detection systems capable of managing vast datasets under dynamic, real-time conditions. The effectiveness of such systems largely depends on the chosen detection methodologies, specifically the feature selection processes and the application of machine learning techniques. This paper offers a comprehensive review of feature selection methods employed in the realm of intrusion detection research. It examines various dimensionality reduction strategies, followed by a systematic classification of feature selection techniques to assess their impact on the training phase and subsequent detection efficacy. The focus was on the wrapper, filter feature selection methods, where the methods used were analysed, and their strengths and weaknesses were revealed. The identification and selection of the most pertinent features have been shown to significantly enhance the detection performance, not only in terms of accuracy but also in reducing computational demands, underscoring its critical importance in the architecture of intrusion detection systems.
Network security , Intrusion detection , Machine learning , Feature selection , Wrapper , Filter
[1] M. Du, “Application of information communication network security management and control based on big data technology,” Int. J. Commun. Syst., vol. 35, no. 5, p. e4643, 2022.
[2] X. Ma, X. Fu, B. Luo, X. Du, and M. Guizani, “A design of firewall based on feedback of intrusion detection system in cloud environment,” in Proc. IEEE Glob. Commun. Conf. (GLOBECOM), Waikoloa, HI, USA, 2019, pp. 1–6.
[3] J. M. Kizza, “System intrusion detection and prevention,” in Guide to Computer Network Security, 6th ed. Springer, 2024, pp. 295–323.
[4] N. A. Azeez, T. M. Bada, S. Misra, A. Adewumi, C. Van der Vyver, and R. Ahuja, “Intrusion detection and prevention systems: An updated review,” in Data Management, Analytics and Innovation (Lecture Notes in Networks and Systems), vol. 1. Springer, 2020, pp. 685–696.
[5] E. F. E. Ahmet and I. N. Abaci, “Comparison of the host based intrusion detection systems and network based intrusion detection systems,” Celal Bayar Univ. J. Sci., vol. 18, no. 1, pp. 23–32, 2022.
[6] Y. Otoum and A. Nayak, “AS-IDS: Anomaly and signature based IDS for the internet of things,” J. Netw. Syst. Manag., vol. 29, no. 3, p. 23, 2021.
[7] M. Schrötter, A. Niemann, and B. Schnor, “A comparison of neural-network-based intrusion detection against signature-based detection in IoT networks,” Information, vol. 15, no. 3, p. 164, 2024.
[8] C.-W. Chen, Y.-H. Tsai, F.-R. Chang, and W.-C. Lin, “Ensemble feature selection in medical datasets: Combining filter, wrapper, and embedded feature selection results,” Expert Syst., vol. 37, no. 5, p. e12553, 2020.
[9] N. Kumar and U. Kumar, “Artificial intelligence for classification and regression tree based feature selection method for network intrusion detection system in various telecommunication technologies,” Comput. Intell., vol. 40, no. 1, p. e12500, 2024.
[10] M. Di Mauro, G. Galatro, G. Fortino, and A. Liotta, “Supervised feature selection techniques in network intrusion detection: A critical review,” Eng. Appl. Artif. Intell., vol. 101, p. 104216, 2021.
[11] F. Rahmat, A. Ali, S. Khan, M. Alazab, and T. R. Gadekallu, “Supervised feature selection using principal component analysis,” Knowl. Inf. Syst., vol. 66, no. 3, pp. 1955–1995, 2024.
[12] S. Bashir, A. S. Khan, Z. S. Khan, and F. A. Khan, “A novel feature selection method for classification of medical data using filters, wrappers, and embedded approaches,” Complexity, vol. 2022, p. 9962023, 2022.
[13] P. Zhu, W. Zuo, L. Zhang, Q. Hu, and S. C. K. Shiu, “Unsupervised feature selection by regularized self-representation,” Pattern Recognit., vol. 48, no. 2, pp. 438–446, 2015.
[14] S. Solorio-Fernández, J. A. Carrasco-Ochoa, and J. F. Martínez-Trinidad, “A review of recent approaches on wrapper feature selection for intrusion detection,” Expert Syst. Appl., vol. 198, p. 116822, 2022.
[15] S. Ganapathy, K. Kulothungan, S. Muthurajkumar, M. Vijayalakshmi, P. Yogesh, and A. Kannan, “Intelligent feature selection and classification techniques for intrusion detection in networks: A survey,” EURASIP J. Wirel. Commun. Netw, vol. 2013, no. 1, p. 271, 2013.
[16] J. Maldonado, M. C. Riff, and B. Neveu, “A review of recent approaches on wrapper feature selection for intrusion detection,” Expert Syst. Appl., vol. 198, p. 116822, 2022.
[17] H. K. Malik, N. J. Al-Anber, and F. A. E. Al-Mekhlafi, “Comparison of feature selection and feature extraction role in dimensionality reduction of big data,” J. Tech., vol. 5, no. 1, pp. 1–8, 2023.
[18] B. K. Padhi, S. Chakravarty, B. Naik, R. M. Pattanayak, and H. Das, “RHSOFS: Feature selection using the rock hyrax swarm optimization algorithm for credit card fraud detection system,” Sensors, vol. 22, no. 23, p. 9321, 2022.
[19] I. B. Adnan, M. A. Khan, and R. A. Khan, “A novel approach for anomaly detection in IoT using hybrid feature selection techniques,” IEEE Access, vol. 12, pp. 12345–12358, 2024.
[20] G. Ansari, T. Ahmad, and M. N. Doja, “Hybrid filter–wrapper feature selection method for sentiment classification,” Arab. J. Sci. Eng., vol. 44, no. 10, pp. 9191–9208, 2019.
[21] J. Abawajy, A. Darem, and A. A. Alhashmi, “Feature subset selection for malware detection in smart IoT platforms,” Sensors, vol. 21, no. 4, p. 1374, 2021.
[22] Y. B. Wah, N. Ibrahim, H. A. Hamid, S. Abdul-Rahman, and S. Fong, “Feature selection methods: Case of filter and wrapper approaches for maximising classification accuracy,” Pertanika J. Sci. Technol., vol. 26, no. 1, pp. 329–340, 2018.
[23] V. Herrera-Semenets, L. Bustio-Martínez, R. Hernández-León, and J. Van den Berg, “A multi-measure feature selection algorithm for efficacious intrusion detection,” Knowl.-Based Syst., vol. 227, p. 107264, 2021.
[24] S. Alhassan, G. Abdul-Salaam, A. Micheal, Y. M. Missah, E. D. Ganaa, and A. S. Shirazu, “CFS-AE: Correlation-based feature selection and autoencoder for improved intrusion detection system performance,” Unpublished.
[25] G. Farahani, “Feature selection based on cross-correlation for the intrusion detection system,” Secur. Commun. Networks, vol. 2020, p. 8844661, 2020.
[26] I. S. Thaseen, J. S. Banu, K. Lavanya, M. R. Ghalib, and K. Abhishek, “An integrated intrusion detection system using correlation-based attribute selection and artificial neural network,” Trans. Emerg. Telecommun. Technol., vol. 32, no. 2, p. e4014, 2021.
[27] R. Zhao, Y. Mu, L. Zou, and X. Wen, “A hybrid intrusion detection system based on feature selection and weighted stacking classifier,” IEEE Access, vol. 10, pp. 71414–71426, 2022.
[28] S. Venkatesan, “Design an intrusion detection system based on feature selection using ML algorithms,” Math. Stat. Eng. Appl., vol. 72, no. 1, pp. 702–710, 2023.
[29] M. J. Siraj, T. Ahmad, and R. M. Ijtihadie, “Analyzing ANOVA F-test and sequential feature selection for intrusion detection systems,” Int. J. Adv. Soft Comput. Its Appl., vol. 14, no. 2, pp. 1–15, 2022.
[30] A. Thakkar and R. Lohiya, “Fusion of statistical importance for feature selection in deep neural network-based intrusion detection system,” Inf. Fusion, vol. 90, pp. 353–363, 2023.
[31] P. Nimbalkar and D. Kshirsagar, “Feature selection for intrusion detection system in Internet-of-Things (IoT),” ICT Express, vol. 7, no. 2, pp. 177–181, 2021.
[32] M. A. Siddiqi and W. Pak, “Optimizing filter-based feature selection method flow for intrusion detection system,” Electronics, vol. 9, no. 12, p. 2114, 2020.
[33] I. Karna, A. Madam, C. Deokule, R. Adhao, and V. Pachghare, “Ensemble-based filter feature selection technique for building flow-based IDS,” in Proc. 2nd Int. Conf. Adv. Comput., Commun., Embedded Secure Syst. (ACCESS), Mumbai, India, 2021, pp. 324–328.
[34] N. El Aboudi and L. Benhlima, “Review on wrapper feature selection approaches,” in Proc. Int. Conf. Eng. MIS (ICEMIS), Agadir, Morocco, 2016, pp. 1–5.
[35] M. Mafarja and S. Mirjalili, “Whale optimization approaches for wrapper feature selection,” Appl. Soft Comput., vol. 62, pp. 441–453, 2018.
[36] B. Nouri-Moghaddam, M. Ghazanfari, and M. Fathian, “A novel multi-objective forest optimization algorithm for wrapper feature selection,” Expert Syst. Appl., vol. 175, p. 114737, 2021.
[37] W. L. Al-Yaseen, A. K. Idrees, and F. H. Almasoudy, “Wrapper feature selection method based differential evolution and extreme learning machine for intrusion detection system,” Pattern Recognit., vol. 132, p. 108912, 2022.
[38] F. H. Almasoudy, W. L. Al-Yaseen, and A. K. Idrees, “Differential evolution wrapper feature selection for intrusion detection system,” Procedia Comput. Sci., vol. 167, pp. 1230–1239, 2020.
[39] E. M. Maseno and Z. Wang, “Hybrid wrapper feature selection method based on genetic algorithm and extreme learning machine for intrusion detection,” J. Big Data, vol. 11, no. 1, p. 24, 2024.
[40] N. Kunhare, R. Tiwari, and J. Dhar, “Intrusion detection system using hybrid classifiers with meta-heuristic algorithms for the optimization and feature selection by genetic algorithm,” Comput. Electr. Eng., vol. 103, p. 108383, 2022.
[41] N. Kunhare, R. Tiwari, and J. Dhar, “Particle swarm optimization and feature selection for intrusion detection system,” Sādhanā, vol. 45, no. 1, p. 109, 2020.
[42] H. Alazzam, A. Sharieh, and K. E. Sabri, “A feature selection algorithm for intrusion detection system based on pigeon inspired optimizer,” Expert Syst. Appl., vol. 148, p. 113249, 2020.
[43] I. Laassar, M. Y. Hadi, H. R. Arifullah, and F. S. Khan, “Proposed algorithm base optimisation plan for feature selection-based intrusion detection in cloud computing,” Indones. J. Electr. Eng. Comput. Sci., vol. 33, no. 2, pp. 1140–1149, 2024.
[44] M. Bakro, A. Al-Sarem, A. Saeed, F. Saeed, W. Elmedany, and M. Alazab, “An improved design for a cloud intrusion detection system using hybrid features selection approach with ML classifier,” IEEE Access, vol. 11, pp. 64228–64247, 2023.
[45] M. A. Umar, Z. Chen, K. Shuaib, and Y. Liu, “Effects of feature selection and normalization on network intrusion detection,” Authorea Prepr., 2024.
[46] R. Lalduhsaka and A. K. Khan, “Enhanced intrusion detection system using a two-staged feature selection method,” Secur. Priv., vol. 8, no. 3, p. e70025, 2025.
[47] M. Umer, M. Tahir, M. Sardaraz, M. Sharif, H. Elmannai, and A. D. Algarni, “Network intrusion detection model using wrapper based feature selection and multi head attention transformers,” Sci. Rep., vol. 15, no. 1, p. 28718, 2025.
