DOS-DDOS Attacks Predicting : Performance Comparison of The Main Feature Selection Strategies

DOS-DDOS Attacks Predicting : Performance Comparison of The Main Feature Selection Strategies

© 2022 by IJETT Journal
Volume-70 Issue-1
Year of Publication : 2022
Authors : Kawtar Bouzoubaa, Youssef Taher, Benayad Nsiri
DOI :  10.14445/22315381/IJETT-V70I1P235

How to Cite?

Kawtar Bouzoubaa, Youssef Taher, Benayad Nsiri, "DOS-DDOS Attacks Predicting : Performance Comparison of The Main Feature Selection Strategies," International Journal of Engineering Trends and Technology, vol. 70, no. 3, pp. 310-318, 2022. Crossref,

Today, cyberattacks are one of the largest threats to individuals and societies across the globe. Dealing with the complexity and variety of these threats by using classical solutions such as software/hardware firewalls and antivirus / antimalware becomes insufficient and presents many drawbacks.
To support and improve the efficiency of these traditional solutions, Machine Learning (ML) models can play an increasing role in detecting, preventing or disrupting cyberattacks at the earliest stage (near real-time).
In this context, the focus of the present paper is to analyze and assess how cyberattacks Feature Selection Strategies (FSS) can support the improvement of these ML models performances applied to cybersecurity, especially the case of DOS-DDOS attacks.
By reviewing more than one hundred and three references and using a hierarchical analysis model based on three levels of performance analysis, this paper has compared the performances of four main types of Feature Selection Methods (TFSM) (first analysis level), the main DOS-DDOS Features Selection Sub-Methods (FSSM) used in each TFSM (second analysis level) and DOS-DDOS datasets widely used in ML cybersecurity projects (third analysis level).

Cybersecurity; DOS-DDOS Attacks; Machine Learning; Feature Selection Strategies.

[1] M. Bada et J. R. C. Nurse, Chapter 4 - The social and psychological impact of cyberattacks, in Emerging Cyber Threats and Cognitive Vulnerabilities, V. Benson et J. Mcalaney, Éd. Academic Press, (2020) 73?92. doi: 10.1016/B978-0-12-816203-3.00004-6.
[2], Tutorial 1: The Impact of Cybercrime on Small Business |, (consulté le oct. 05, 2021).
[3] B. Cashell, W. D. Jackson, M. Jickling, et B. Webel, The Economic Impact of Cyber-Attacks, (2004).
[4] Y. V. Srinivasa Murthy, K. Harish, V. Varma, K. Sriram, et B. Revanth, Hybrid Intelligent Intrusion Detection System using Bayesian and Genetic Algorithm (BAGA): Comparative Study, International Journal of Computer Applications, 99 (2014) 1?8, doi: 10.5120/17342-7808.
[5] J. Sen et S. Mehtab, Machine Learning Applications in Misuse and Anomaly Detection, in Machine Learning Applications in Misuse and Anomaly Detection, IntechOpen, (2020). doi: 10.5772/intechopen.92653.
[6] N. Alqudah et Q. Yaseen, Machine Learning for Trafic Analysis: A Review, Warsaw Poland, 170 (2020) 911?916.
[7] K. Shaukat, S. Luo, V. Varadharajan, I. A. Hameed, et M. Xu, A Survey on Machine Learning Techniques for Cyber Security in the Last Decade, IEEE Access, 8 (2020) 222310?222354, doi: 10.1109/ACCESS.2020.3041951.
[8] R. Abdulhammed, H. Musafer, A. Alessa, M. Faezipour, et A. Abuzneid, Features Dimensionality Reduction Approaches for Machine Learning Based Network Intrusion Detection, Electronics, 8(3) (2019), doi: 10.3390/electronics8030322.
[9] N. Moustafa et J. Slay, UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set), in 2015 Military Communications and Information Systems Conference (MilCIS), Canberra, Australia, (2015) 1?6. doi: 10.1109/MilCIS.2015.7348942.
[10] S. K. Sahu, S. Sarangi, et S. K. Jena, A detailed analysis on intrusion detection datasets, in 2014 IEEE International Advance Computing Conference (IACC), févr. (2014) 1348?1353. doi: 10.1109/IAdCC.2014.6779523.
[11] D. H. Hagos, A. Yazidi, Ø. Kure, et P. E. Engelstad, Enhancing Security Attacks Analysis Using Regularized Machine Learning Techniques, in IEEE 31st International Conference on Advanced Information Networking and Applications (AINA), mars (2017) 909?918. doi: 10.1109/AINA.2017.19.
[12] L. Dhanabal et S. P. Shantharajah, A Study on NSL-KDD Dataset for Intrusion Detection System Based on Classification Algorithms, International Journal of Advanced Research in Computer and Communication Engineering, 4(6) (2015) 446, doi: 10.17148/IJARCCE.2015.4696y.
[13] A. Thakkar et R. Lohiya, A Review of the Advancement in Intrusion Detection Datasets, Procedia Computer Science, 167 (2020) 636?645, doi: 10.1016/j.procs.2020.03.330.
[14] J. Miao et L. Niu, A Survey on Feature Selection, Procedia Computer Science, 91 (2016) 919?926, doi: 10.1016/j.procs.2016.07.111.
[15] Y. Feng, H. Akiyama, L. Lu, et K. Sakurai, Feature Selection for Machine Learning-Based Early Detection of Distributed Cyber Attacks, in 2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech), août (2018) 173?180. doi: 10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.00040.
[16] W. Mostert, K. M. Malan, et A. P. Engelbrecht, A Feature Selection Algorithm Performance Metric for Comparative Analysis, Algorithms, 14(3) (2021), doi: 10.3390/a14030100.
[17] M. Torabi, N. I. Udzir, M. T. Abdullah, et R. Yaakob, A Review on Feature Selection and Ensemble Techniques for Intrusion Detection System, International Journal of Advanced Computer Science and Applications, 12(5) (2021)16.
[18] V. R. Balasaraswathi, M. Sugumaran, et Y. Hamid, Feature selection techniques for intrusion detection using non-bio-inspired and bio-inspired optimization algorithms, J. Commun. Inf. Netw., 2(4) (2017) 107?119, doi: 10.1007/s41650-017-0033-7.
[19] S. Wang, J. Tang, et H. Liu, Feature Selection, (2016) 1?9. doi: 10.1007/978-1-4899-7502-7_101-1.
[20] H. Bostani et M. Sheikhan, Hybrid of binary gravitational search algorithm and mutual information for feature selection in intrusion detection systems, Soft Comput, 21(9) (2017) 2307?2324 doi: 10.1007/s00500-015-1942-8.
[21] S. Solorio-Fernández, J. A. Carrasco-Ochoa, et J. Fco. Martínez-Trinidad, A review of unsupervised feature selection methods , Artif Intell Rev, 53(2) (2017) 907?948, X doi: 10.1007/s10462-019-09682-y.
[22] S. Murugesan, Application of Machine Learning Models for Network Intrusion Detection Systems Based on Feature Selection Approach, masters, Dublin, National College of Ireland, (2019) (2021). [En ligne]. Disponible sur:
[23] M. Idhammad, K. Afdel, et M. Belouch, DoS Detection Method based on Artificial Neural Networks, International Journal of Advanced Computer Science and Applications (IJACSA), 8(4) (2017), doi: 10.14569/IJACSA.2017.080461.
[24] M. Labonne, Anomaly-based network intrusion detection using machine learning, Institut Polytechnique de Paris, (2020).
[25] R. Magán-Carrión, D. Urda, I. Diaz-Cano, et B. Dorronsoro, Towards a Reliable Comparison and Evaluation of Network Intrusion Detection Systems Based on Machine Learning Approaches, Applied Sciences, 10 (2020) 1775, doi: 10.3390/app10051775.
[26] A. Khraisat, I. Gondal, P. Vamplew, et J. Kamruzzaman, Survey of intrusion detection systems: techniques, datasets and challenges, Cybersecur, 2(1) (2019) 20, doi: 10.1186/s42400-019-0038-7.
[27] A. I. Madbouly et T. M. Barakat, Enhanced relevant feature selection model for intrusion detection systems, Int. J. Intell. Eng. Inform., 4(1) (2016) 21?45, doi: 10.1504/IJIEI.2016.074499.
[28] M. H. Kamarudin, C. Maple, et T. Watson, Hybrid feature selection technique for intrusion detection system, International Journal of High-Performance Computing and Networking, 13 (2019) 232, doi: 10.1504/IJHPCN.2019.097503.
[29] B. Kavitha, Dr. S. Karthikeyan, et P. Sheeba Maybell, An Ensemble Design of Intrusion Detection System for Handling Uncertainty Using Neutrosophic Logic Classifier, Know.-Based Syst., 28 (2012) 88?96, doi: 10.1016/j.knosys.2011.12.004.
[30] M. Babiker, E. Karaarslan, et Y. Ho?can, A hybrid feature-selection approach for finding the digital evidence of web application attacks, Turkish Journal of Electrical Engineering and Computer Sciences, 27 (2019) 4102?4117, doi: 10.3906/elk-1812-18.
[31] M. H. Bhuyan, D. K. Bhattacharyya, et J. K. Kalita, A multi-step outlier-based anomaly detection approach to network-wide traffic, Information Sciences, 348 (2016) 243?271, doi: 10.1016/j.ins.2016.02.023.
[32] M. A. Ambusaidi, X. He, P. Nanda, et Z. Tan, Building an Intrusion Detection System Using a Filter-Based Feature Selection Algorithm, IEEE Trans. Comput., 65(10) (2016) 2986?2998, doi: 10.1109/TC.2016.2519914.
[33] A. Binbusayyis et T. Vaiyapuri, Identifying and Benchmarking Key Features for Cyber Intrusion Detection: An Ensemble Approach, IEEE Access, 7 (2019) 106495?106513, doi: 10.1109/ACCESS.2019.2929487.
[34] D. Srivastava, Feature Classification and Outlier Detection to Increased Accuracy in Intrusion Detection System, International Journal of Applied Engineering Research, vol. 13, p. 7249?7255, nov. 2018.
[35] B. A. Manjunatha et M. T. Gogoi, Data Mining based Framework for Effective Intrusion Detection using Hybrid Feature Selection Approach, IJCNIS, 11(8) (2019) 1?12, doi: 10.5815/ijcnis.2019.08.01.
[36] T. Ahmad et M. N. Aziz, Data preprocessing and feature selection for machine learning intrusion detection systems, ICIC Express Letters, 13 (2019) 93?101, doi: 10.24507/icicel.13.02.93.
[37] M. B. Shahbaz, X. Wang, A. Behnad, et J. Samarabandu, On efficiency enhancement of the correlation-based feature selection for intrusion detection systems, in IEEE 7th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), (2016) 1?7. doi: 10.1109/IEMCON.2016.7746286.
[38] M. Bataghva, Efficiency and Accuracy Enhancement of Intrusion Detection System Using Feature Selection and Cross-layer Mechanism, Electronic Thesis and Dissertation Repository, University of Western Ontario, Ontario, (2017). [En ligne]. Disponible sur:
[39] T. H. Divyasree et K. K. Sherly, A Network Intrusion Detection System Based On Ensemble CVM Using Efficient Feature Selection Approach, Procedia Computer Science, 143 (2018) 442?449, doi: 10.1016/j.procs.2018.10.416.
[40] G. P. Gupta et M. Kulariya, A Framework for Fast and Efficient Cyber Security Network Intrusion Detection Using Apache Spark, Procedia Computer Science, 93 (2017) 824?831, doi: 10.1016/j.procs.2016.07.238.
[41] M. Abdullah, A. Balamash, A. Al-Shannaq, et S. Almabdy, Enhanced Intrusion Detection System using Feature Selection Method and Ensemble Learning Algorithms, International Journal of Computer Science and Information Security, 16 (2018) 48?55.
[42] Z. Foroushani et Y. Li, Intrusion Detection System by Using Hybrid Algorithm of Data Mining Technique, in ICSCA 2018: Proceedings of the 2018 7th International Conference on Software and Computer Applications, Kuantan, Malaysia, févr. (2018) 119?123. doi: 10.1145/3185089.3185114.
[43] S. M. Othman, F. M. Ba-Alwi, N. T. Alsohybe, et A. Y. Al-Hashida, Intrusion detection model using machine learning algorithm on Big Data environment, Journal of Big Data, 5(1) (2018) 34, doi: 10.1186/s40537-018-0145-4.
[44] Q. R. S. Fitni et K. Ramli, Implementation of Ensemble Learning and Feature Selection for Performance Improvements in Anomaly-Based Intrusion Detection Systems, in IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT), (2020) 118?124. doi: 10.1109/IAICT50021.2020.9172014.
[45] B. Setiawan, S. Djanali, et T. Ahmad, Increasing Accuracy and Completeness of Intrusion Detection Model Using Fusion of Normalization, Feature Selection Method and Support Vector Machine, International Journal of Intelligent Engineering and Systems, 12 (2019) 378?389, doi: 10.22266/ijies2019.0831.35.
[46] K. K. Myint et N. S. M. Kham, Feature Selection in Hybrid Intrusion Detection System, févr. (2016) (2021). [En ligne]. Disponible sur:
[47] Kurniabudi, D. Stiawan, Darmawijoyo, M. Y. Bin Idris, A. M. Bamhdi, et R. Budiarto, CICIDS-2017 Dataset Feature Analysis With Information Gain for Anomaly Detection, IEEE Access, 8 (2020) 132911?132921, doi: 10.1109/ACCESS.2020.3009843.
[48] M. Hammad, N. Hewahi, et W. Elmedany, T-SNERF: A novel high accuracy machine learning approach for Intrusion Detection Systems », IET Information Security, 15(2) (2021) 178?190, doi:
[49] D. Kshirsagar et S. Kumar, An efficient feature reduction method for the detection of DoS attack, ICT Express, (2021), doi: 10.1016/j.icte.2020.12.006.
[50] J. Yogendra Kumar et Upendra, Intrusion Detection using Supervised Learning with Feature Set Reduction, International Journal of Computer Applications, 33(6) (2011) 22?31.
[51] Akashdeep, I. Manzoor, et N. Kumar, A feature reduced intrusion detection system using ANN classifier, Expert Systems with Applications, 88, (2017) 249?257, doi: 10.1016/j.eswa.2017.07.005.
[52] G. Farahani, Feature Selection Based on Cross-Correlation for the Intrusion Detection System, Security and Communication Networks, vol. 2020, p. e8875404, sept. 2020, doi: 10.1155/2020/8875404.
[53] Y. Wahba, E. ElSalamouny, et G. ElTaweel, Improving the Performance of Multi-class Intrusion Detection Systems using Feature Reduction, International Journal of Computer Science Issues, 12(3) (2015) 255?262.
[54] M. A. Siddiqi et W. Pak, Optimizing Filter-Based Feature Selection Method Flow for Intrusion Detection System, Electronics, 9(12) (2020), doi: 10.3390/electronics9122114.
[55] S. Sambangi et L. Gondi, A Machine Learning Approach for DDoS (Distributed Denial of Service) Attack Detection Using Multiple Linear Regression, Proceedings, 63(1) (2020), doi: 10.3390/proceedings2020063051.
[56] A. Binbusayyis et T. Vaiyapuri, Comprehensive analysis and recommendation of feature evaluation measures for intrusion detection, Heliyon, 6(7) (2020), doi: 10.1016/j.heliyon.2020.e04262.
[57] K. Bouzoubaa, Y. Taher, et B. Nsiri, Predicting DOS-DDOS Attacks: Review and Evaluation Study of Feature Selection Methods based on Wrapper Process, International Journal of Advanced Computer Science and Applications, (2021).
[58] S.-W. Lin, K. Ying, C. Lee, et Z.-J. Lee, An intelligent algorithm with feature selection and decision rules applied to anomaly intrusion detection, Appl. Soft Comput., 12(10) (2012) 3285?3290, doi: 10.1016/j.asoc.2012.05.004.
[59] M. A. Umar et Z. Chen, Effects of Feature Selection and Normalization on Network Intrusion Detection. (2020). doi: 10.36227/techrxiv.12480425.
[60] C. Khammassi et S. Krichen, A GA-LR wrapper approach for feature selection in network intrusion detection, Computers & Security, vol. 70 (2017) 255?277, doi: 10.1016/j.cose.2017.06.005.
[61] J. Lee, D. Park, et C. Lee, Feature Selection Algorithm for Intrusions Detection System using Sequential Forward Search and Random Forest Classifier, KSII Transactions on Internet and Information Systems, 11(10) (2017) 5132?5148.
[62] M. A. Umar et C. Zhanfang, Effects of Feature Selection and Normalization on Network Intrusion Detection, (2020), doi: 10.36227/techrxiv.12480425.v2.
[63] M. Hosseinzadeh Aghdam et P. Kabiri, Feature Selection for Intrusion Detection System Using Ant Colony Optimization, International Journal of Network Security, 18 (2016) 420?432.
[64] H. Soodeh et A. Mehrdad, The hybrid technique for DDoS detection with supervised learning algorithms, Computer Networks, 158 (2019) 35?45, doi: 10.1016/j.comnet.2019.04.027.
[65] D. P. Gaikwad et R. C. Thool, Intrusion Detection System Using Bagging with Partial Decision TreeBase Classifier, Procedia Computer Science, 49 (2015) 92?98, doi: 10.1016/j.procs.2015.04.231.
[66] M. Mazini, B. Shirazi, et I. Mahdavi, Anomaly network-based intrusion detection system using a reliable hybrid artificial bee colony and AdaBoost algorithms, Journal of King Saud University - Computer and Information Sciences, 31(4) (2019) 541?553, doi: 10.1016/j.jksuci.2018.03.011.
[67] O. Y. Al-Jarrah, A. Siddiqui, M. Elsalamouny, P. D. Yoo, S. Muhaidat, et K. Kim, Machine-Learning-Based Feature Selection Techniques for Large-Scale Network Intrusion Detection, in IEEE 34th International Conference on Distributed Computing Systems Workshops (ICDCSW), (2014) 177?181. doi: 10.1109/ICDCSW.2014.14.
[68] B. Kavitha, S. Karthikeyan, et B. Chitra, Efficient Intrusion Detection with Reduced Dimension Using Data Mining Classification Methods and Their Performance Comparison, in Information Processing and Management, V. V. Das, R. Vijayakumar, N. C. Debnath, J. Stephen, N. Meghanathan, S. Sankaranarayanan, P. M. Thankachan, F. L. Gaol, et N. Thankachan, Éd. Berlin, Heidelberg: Springer Berlin Heidelberg, 70 (2010) 96?101. doi: 10.1007/978-3-642-12214-9_17.
[69] S. Alabdulwahab et B. Moon, Feature Selection Methods Simultaneously Improve the Detection Accuracy and Model Building Time of Machine Learning Classifiers, Symmetry, 12(9) (2020), doi: 10.3390/sym12091424.
[70] F. Almasoudy, W. Al-Yaseen, et A. Idrees, Differential Evolution Wrapper Feature Selection for Intrusion Detection System, Procedia Computer Science, 167 (2019) 1230?1239, doi: 10.1016/j.procs.2020.03.438.
[71] M. Alrowaily, F. Alenezi, et Z. Lu, Effectiveness of Machine Learning Based Intrusion Detection Systems, in Security, Privacy, and Anonymity in Computation, Communication, and Storage, Cham, (2019) 277?288. doi: 10.1007/978-3-030-24907-6_21.
[72] C. Yin, L. Ma, et L. Feng, Towards accurate intrusion detection based on improved clonal selection algorithm, Multimed Tools Appl, 76(19) (2017) 19397?19410, , doi: 10.1007/s11042-015-3117-0.
[73] L. Yinhui, J. Xia, S. Zhang, J. Yan, X. Ai, et K. Dai, An efficient intrusion detection system based on support vector machines and gradually feature removal method, Expert Systems with Applications, 39 (2012) 424?430, doi: 10.1016/j.eswa.2011.07.032.
[74] W. Xing-zhu et H. Changde, ACO and SVM Selection Feature Weighting of Network Intrusion Detection Method, 9 (2015) 129?270. doi: 10.14257/ijsia.2015.9.4.24.
[75] M. Samadi Bonab, A. Ghaffari, F. Soleimanian Gharehchopogh, et P. Alemi, A wrapper?based feature selection for improving performance of intrusion detection systems, International Journal of Communication Systems, 33 (2020), doi: 10.1002/dac.4434.
[76] F. Zhang et D. Wang, An Effective Feature Selection Approach for Network Intrusion Detection, in 2013 IEEE Eighth International Conference on Networking, Architecture and Storage, (2013) 307?311. doi: 10.1109/NAS.2013.49.
[77] M. N. Chowdhury, K. Ferens, et M. Ferens, Network Intrusion Detection Using Machine Learning, in Computer Science, (2016) 30?35. [En ligne]. Disponible sur: /paper/Network-Intrusion-Detection-Using-Machine-Learning-Chowdhury-Ferens/a30c16f5598ba18ffd7d9c533515cf671d54b382
[78] H. Polat, O. Polat, et A. Cetin, Detecting DDoS Attacks in Software-Defined Networks Through Feature Selection Methods and Machine Learning Models, Sustainability, 12(3) (2020) 1?16.
[79] M. A. Umar, C. Zhanfang, et Y. Liu, A Hybrid Intrusion Detection with Decision Tree for Feature Selection, arXiv:2009.13067 [cs], Consulté le, (2021). [En ligne]. Disponible sur:
[80] A. Enache, V. Sgârciu, et M. Togan, Comparative Study on Feature Selection Methods Rooted in Swarm Intelligence for Intrusion Detection, in 2017 21st International Conference on Control Systems and Computer Science (CSCS), (2017) 239?244. doi: 10.1109/CSCS.2017.40.
[81] W. Jun, L. Taihang, et R. Rongrong, A real time IDSs based on artificial Bee Colony-support vector machine algorithm, Suzhou, Jiangsu, China, (2010) 91?96. doi: 10.1109/IWACI.2010.5585107.
[82] M. H. Kamarudin, C. Maple, T. Watson, et N. S. Safa, A LogitBoost-Based Algorithm for Detecting Known and Unknown Web Attacks, IEEE Access, 5 (2017) 26190?26200, doi: 10.1109/ACCESS.2017.2766844.
[83] J. Song, W. Zhao, Q. Liu, et X. Wang, Hybrid feature selection for supporting lightweight intrusion detection systems, J. Phys.: Conf. Ser., 887 (2017) 012031, doi: 10.1088/1742-6596/887/1/012031.
[84] Y. Zhou, G. Cheng, S. Jiang, et M. Dai, An Efficient Intrusion Detection System Based on Feature Selection and Ensemble Classifier. (2019).
[85] Y. Gu, K. Li, Z. Guo, et Y. Wang, Semi-Supervised K-Means DDoS Detection Method Using Hybrid Feature Selection Algorithm, IEEE Access, 7 (2019) 64351?64365, 2019, doi: 10.1109/ACCESS.2019.2917532.
[86] S. Bagui, E. Kalaimannan, S. Bagui, D. Nandi, et A. Pinto, Using machine learning techniques to identify rare cyber?attacks on the UNSW?NB15 dataset, Security and Privacy, 2 (2019), doi: 10.1002/spy2.91.
[87] I. S. Thaseen et Ch. A. Kumar, Intrusion Detection Model Using Chi Square Feature Selection and Modified Naïve Bayes Classifier, in Proceedings of the 3rd International Symposium on Big Data and Cloud Computing Challenges (ISBCC – 16’), Cham, (2016) 81?91. doi: 10.1007/978-3-319-30348-2_7.
[88] I. Sumaiya Thaseen et C. Aswani Kumar, Intrusion detection model using fusion of chi-square feature selection and multi class SVM, Journal of King Saud University - Computer and Information Sciences, vol. 29(4) (2017) 462?472, doi: 10.1016/j.jksuci.2015.12.004.
[89] A. Kumar K S, A. K., L. M. N., et S. M., A Novel Approach for Intrusion Detection System Using feature Selection algorithm, 13 (2017) 1963?1976.
[90] R. Venkatarathinam, Dr. V. Cyril Raj, et Dr. V. Victo Sudha George, A Novel Hybrid Iterative Backward Feature Selection Framework for Intrusion Detection System, International Journal of Applied Engineering Research, 13(5) (2018) 2780?2785.
[91] S. Mohammadi, H. Mirvaziri, M. Ghazizadeh-Ahsaee, et H. Karimipour, Cyber intrusion detection by combined feature selection algorithm, Journal of Information Security and Applications, 44 (2019) 80?88, doi: 10.1016/j.jisa.2018.11.007.
[92] E. Serkani, H. Gharaee Garakani, et N. Mohammadzadeh, Anomaly Detection Using SVM as Classifier and Decision Tree for Optimizing Feature Vectors, The ISC International Journal of Information Security, 11(2) (2019) 159?171., doi: 10.22042/isecure.2019.164980.448.
[93] X. Li, P. Yi, W. Wei, Y. Jiang, et L. Tian, LNNLS-KH: A Feature Selection Method for Network Intrusion Detection, Security and Communication Networks, (2021) e8830431, doi: 10.1155/2021/8830431.
[94] C. Guo, Y. Zhou, Y. Ping, Z. Zhang, G. Liu, et Y. Yang, A distance sum-based hybrid method for intrusion detection, Applied Intelligence, 40 (2014), doi: 10.1007/s10489-013-0452-6.
[95] A. S. Alzahrani, R. A. Shah, Y. Qian, et M. Ali, A novel method for feature learning and network intrusion classification, Alexandria Engineering Journal, 59(3) (2020) 1159?1169, doi: 10.1016/j.aej.2020.01.021.
[96] N. Acharya et S. Singh, An IWD-based feature selection method for intrusion detection system, Soft Comput, vol. 22, no 13, p. 4407?4416, juill. 2018, doi: 10.1007/s00500-017-2635-2.
[97] H. Nkiama, S. Z. M. Said, et M. Saidu, A Subset Feature Elimination Mechanism for Intrusion Detection System, International Journal of Advanced Computer Science and Applications (IJACSA), 7(4) (2016), doi: 10.14569/IJACSA.2016.070419.