الگوریتم‌های فراابتکاری برای انتخاب ویژگی در سیستمهای تشخیص نفوذ: یک بررسی سیستماتیک
محورهای موضوعی : پردازش چند رسانه ای، سیستمهای ارتباطی، سیستمهای هوشمندیاشار پور اردبیل خواه 1 , میر سعید حسینی شیروانی 2 , همایون مؤتمنی 3
1 - دانشجوی دکتری، گروه مهندسی کامپیوتر، دانشکده فنی مهندسی، دانشگاه آزاد اسلامی، ساری، ایران
2 - استادیار، گروه مهندسی کامپیوتر، واحد ساری، دانشگاه آزاد اسلامی، ساری، ایران
3 - استاد، گروه مهندسی کامپیوتر، واحد ساری، دانشگاه آزاد اسلامی، ساری، ایران
کلید واژه: انتخاب ویژگی, الگوریتم فراابتکاری, سیستم تشخیص نفوذ, رایانش ابری, بهینه‌سازی, امنیت شبکه,
چکیده مقاله :
با توجه به اهمیت روزافزون امنیت شبکه در محیط‌های پیچیده و پویا، سیستمهای تشخیص نفوذ نقش بسیار مهمی در شناسایی و مقابله با تهدیدات امنیتی ایفامی‌کنند. با این حال، وجود حجم زیادی از داده‌ها در شبکه‌ها، کارایی سیستمهای تشخیص نفوذ را تحت تأثیر قرارمی‌دهد. انتخاب ویژگی به عنوان یک مرحله حیاتی در پیش‌پردازش داده‌ها می‌تواند به بهبود دقت، سرعت و کارایی این سیستم‌ها کمک کند. این مقاله به بررسی سیستماتیک روش‌های انتخاب ویژگی مبتنی بر الگوریتم‌های فراابتکاری در سیستمهای تشخیص نفوذ در محیط رایانش ابری می‌پردازد. روش کار شامل مرور جامعی بر تحقیقات انجام‌شده در حوزه انتخاب ویژگی برای سیستمهای تشخیص نفوذ است. در این بررسی، الگوریتم‌های فراابتکاری مانند الگوریتم ژنتیک، بهینه‌سازی ازدحام ذرات ، بهینه‌سازی کلونی زنبور، الگوریتم خفاش، و سایر روش‌های بهینه‌سازی الهام گرفته از طبیعت، به طور کامل بررسی شده‌اند. این الگوریتم‌ها به دلیل توانایی‌شان در جستجوی فضای بزرگ ویژگی‌ها و شناسایی بهترین ترکیب‌ها برای بهبود عملکرد سیستمهای تشخیص نفوذ، انتخاب شده‌اند. در این مقاله، مقایسه‌های دقیقی بین الگوریتم‌های مختلف از نظر معیارهای عملکردی همچون دقت تشخیص، نرخ هشدار غلط، زمان پردازش و تعداد ویژگی‌های انتخاب‌شده انجام شده است. همچنین، مجموعه داده‌های مختلفی در این زمینه، مورد بحث و بررسی قرار گرفته‌اند تا کارایی هر یک از روش‌ها در شرایط مختلف ارزیابی شود. علاوه بر این، چالش‌های کلیدی در این حوزه مورد شناسایی و تحلیل قرار گرفته‌اند. این چالش‌ها شامل مواردی همچون پیچیدگی محاسباتی بالا، مسئله سربار پردازشی در محیط‌های ابری، تعادل بین دقت و سرعت تشخیص، و مشکل تداخل ویژگی‌ها هستند. همچنین، شکاف‌های تحقیقاتی موجود در این زمینه که نیازمند تحقیقات بیشتر است، شناسایی شده‌اند. نتایج این بررسی نشان می‌دهد که هریک از الگوریتم‌های فراابتکاری دارای نقاط قوت و ضعف خاص خود هستند و انتخاب بهترین روش بستگی به نیازمندی‌ها و شرایط خاص سیستم دارد. این مقاله با ارائه توصیه‌هایی برای تحقیقات آینده، به عنوان یک راهنمای جامع برای پژوهشگران و توسعه‌دهندگان سیستمهای تشخیص نفوذ در محیط‌های ابری مطرح می‌شود.
Introduction: Considering the increasing importance of network security in complex and dynamic environments, intrusion detection systems (IDS) play a very important role in identifying and dealing with security threats. However, the presence of a large amount of data in networks affects the efficiency of intrusion detection systems. Feature selection as a critical step in data preprocessing can help improve the accuracy, speed, and efficiency of these systems. This article deals with the systematic review of feature selection methods based on meta-heuristic algorithms in intrusion detection systems in the cloud computing environment.
Method: The methodology of this article includes a comprehensive review of the research conducted in the field of feature selection for IDS. In this review, meta-heuristic algorithms such as genetic algorithm, particle swarm optimization (PSO), bee colony optimization, bat algorithm, and other nature-inspired optimization methods are thoroughly reviewed. These algorithms are chosen due to their ability to search a large space of features and identify the best combinations to improve the performance of IDSs.
Evaluation: In this paper, detailed comparisons have been made between different algorithms in terms of performance criteria such as detection accuracy, false alarm rate, processing time and the number of selected features. Also, different data sets that have been used in this field have been discussed to evaluate the efficiency of each of the methods in different conditions.
Challenge: In addition, key challenges in this field have been identified and analyzed. These challenges include things such as high computational complexity, the problem of processing overhead in cloud environments, the balance between accuracy and detection speed, and the problem of feature interference. Also, research gaps in this field that require further research have been identified.
1. Thakkar, A., & Lohiya, R. (2022). A survey on intrusion detection system: feature selection, model, performance measures, application perspective, challenges, and future research directions. Artificial Intelligence Review, 55(1), 453-563. https://doi.org/10.1007/S10462-021-10037-9.
2. Ghaffari, A., & Hossinnezhad, R. (2022).Intrusions detection system in the cloud computing using heterogeneity detection technique.Intelligent Multimedia Processing and Communication Systems (IMPCS),3(1),37-46.
https://srb.sanad.iau.ir/en/Article/903516
3. Roknaldini, M., & Noroozi, E. (1402). Presenting A Hybrid Method of Deep Neural Networks to Prevent Intrusion in Computer Networks. Intelligent Multimedia Processing and Communication Systems (IMPCS), 4(4), 57-65. http://sanad.iau.ir/fa/Article/903465
4. Kaya, İ. (2009). RETRACTED: A genetic algorithm approach to determine the sample size for control charts with variables and attributes. https://doi.org/10.1016/j.eswa.2008.12.011
5. Mohammad Akhlaghpour. (2023,apr). Using the Modified Colonial Competition Algorithm to Increase the Speed and Accuracy of the Intelligent Intrusion Detection System. (IMPCS), (pp. 1-10). https://doi.org/10.1016/j.cie.2019.106040
6. Khanduja, N., & Bhushan, B. (2021). Recent advances and application of metaheuristic algorithms: A survey (2014–2020). Metaheuristic and evolutionary computation: algorithms and applications, 207-228. https://doi.org/10.1007/978-981-15-7571-6_10.
7. Dokeroglu, T., Deniz, A., & Kiziloz, H. E. (2022). A comprehensive survey on recent metaheuristics for feature selection. Neurocomputing, 494, 269-296. https://doi.org/10.1016/j.neucom.2022.04.083.
8. Maldonado, J., Riff, M. C., & Neveu, B. (2022). A review of recent approaches on wrapper feature selection for intrusion detection. Expert Systems with Applications, 198, 116822. https://doi.org/10.1016/j.eswa.2022.116822.
9. Sharma, S., Kumar, V., & Dutta, K. (2024). Multi-objective optimization algorithms for intrusion detection in IoT networks: A systematic review. Internet of Things and Cyber-Physical Systems. https://doi.org/10.1016/j.iotcps.2024.01.003.
10. Reddy, D. K. K., Nayak, J., Behera, H. S., Shanmuganathan, V., Viriyasitavat, W., & Dhiman, G. (2024). A Systematic Literature Review on Swarm Intelligence Based Intrusion Detection System: Past, Present and Future. Archives of Computational Methods in Engineering, 1-68. https://doi.org/10.1007/s11831-023-10059-2.
11. Balasaraswathi, V. R., Sugumaran, M., & Hamid, Y. (2017). Feature selection techniques for intrusion detection using non-bio-inspired and bio-inspired optimization algorithms. Journal of Communications and Information Networks, 2, 107-119. https://doi.org/10.1007/s41650-017-0033-7.
12. Saadouni, R., Gherbi, C., Aliouat, Z., Harbi, Y., & Khacha, A. (2024). Intrusion detection systems for IoT based on bio-inspired and machine learning techniques: a systematic review of the literature. Cluster Computing, 1-27. https://doi.org/10.1007/s10586-024-04388-5.
13. Kalimuthan, C., & Renjit, J. A. (2020). Review on intrusion detection using feature selection with machine learning techniques. Materials Today: Proceedings, 33, 3794-3802. https://doi.org/10.1016/j.matpr.2020.06.218.
14. Agrawal, P., Abutarboush, H. F., Ganesh, T., & Mohamed, A. W. (2021). Metaheuristic algorithms on feature selection: A survey of one decade of research (2009-2019). Ieee Access, 9, 26766-26791. https://doi.org/10.1109/ACCESS.2021.3056407.
15. Nssibi, M., Manita, G., & Korbaa, O. (2023). Advances in nature-inspired metaheuristic optimization for feature selection problem: A comprehensive survey. Computer Science Review, 49, 100559. https://doi.org/10.1016/j.cosrev.2023.100559.
16. Pham, T. H., & Raahemi, B. (2023). Bio-inspired feature selection algorithms with their applications: a systematic literature review. IEEE Access. https://doi.org/10.1109/ACCESS.2023.3272556.
17. Bello, S. A., Oyedele, L. O., Akinade, O. O., Bilal, M., Delgado, J. M. D., Akanbi, L. A., ... & Owolabi, H. A. (2021). Cloud computing in construction industry: Use cases, benefits and challenges. Automation in Construction, 122, 103441. https://doi.org/10.1016/j.autcon.2020.103441.
18. Lansky, J., Ali, S., Mohammadi, M., Majeed, M. K., Karim, S. H. T., Rashidi, S., ... & Rahmani, A. M. (2021). Deep learning-based intrusion detection systems: a systematic review. IEEE Access, 9, 101574-101599. https://doi.org/10.1109/ACCESS.2021.3097247
19. Melvin, A. A. R., Kathrine, G. J. W., Ilango, S. S., Vimal, S., Rho, S., Xiong, N. N., & Nam, Y. (2022). Dynamic malware attack dataset leveraging virtual machine monitor audit data for the detection of intrusions in cloud. Transactions on Emerging Telecommunications Technologies, 33(4), e4287. https://doi.org/10.1002/ett.4287.
20. Rostami, M., Berahmand, K., Nasiri, E., & Forouzandeh, S. (2021). Review of swarm intelligence-based feature selection methods. Engineering Applications of Artificial Intelligence, 100, 104210.
https://doi.org/10.1016/j.engappai.2021.104210.
21. Solorio-Fernández, S., Carrasco-Ochoa, J. A., & Martínez-Trinidad, J. F. (2020). A review of unsupervised feature selection methods. Artificial Intelligence Review, 53(2), 907-948.
https://doi.org/10.1007/s10462-019-09682-y.
22. Abdel-Basset, M., Abdel-Fatah, L., & Sangaiah, A. K. (2018). Metaheuristic algorithms: A comprehensive review. Computational intelligence for multimedia big data on the cloud with engineering applications, 185-231. https://doi.org/10.1016/B978-0-12-813314-9.00010-4.
23. Osaba, E., Villar-Rodriguez, E., Del Ser, J., Nebro, A. J., Molina, D., LaTorre, A., ... & Herrera, F. (2021). A tutorial on the design, experimentation and application of metaheuristic algorithms to real-world optimization problems. Swarm and Evolutionary Computation, 64, 100888. https://doi.org/10.1016/j.swevo.2021.100888.
24. Dehghani, M., Montazeri, Z., Trojovská, E., & Trojovský, P. (2023). Coati Optimization Algorithm: A new bio-inspired metaheuristic algorithm for solving optimization problems. Knowledge-Based Systems, 259, 110011. https://doi.org/10.1016/j.knosys.2022.110011.
25. Priyadarshini, J., Premalatha, M., Čep, R., Jayasudha, M., & Kalita, K. (2023). Analyzing physics-inspired metaheuristic algorithms in feature selection with K-nearest-neighbor. Applied Sciences, 13(2), 906. https://doi.org/10.3390/app13020906.
26. Jia, H., & Lu, C. (2024). Guided learning strategy: A novel update mechanism for metaheuristic algorithms design and improvement. Knowledge-Based Systems, 286, 111402. https://doi.org/10.1016/j.knosys.2024.111402.
27. Kwakye, B. D., Li, Y., Mohamed, H. H., Baidoo, E., & Asenso, T. Q. (2024). Particle guided metaheuristic algorithm for global optimization and feature selection problems. Expert Systems with Applications, 248, 123362. https://doi.org/10.1016/j.eswa.2024.123362.
28. Di Mauro, M., Galatro, G., Fortino, G., & Liotta, A. (2021). Supervised feature selection techniques in network intrusion detection: A critical review. Engineering Applications of Artificial Intelligence, 101, 104216. https://doi.org/10.1016/j.engappai.2021.104216
29. Nazir, A., & Khan, R. A. (2021). A novel combinatorial optimization based feature selection method for network intrusion detection. Computers & Security, 102, 102164. https://doi.org/10.1016/j.cose.2020.102164.
30. Slowik, A., & Kwasnicka, H. (2020). Evolutionary algorithms and their applications to engineering problems. Neural Computing and Applications, 32, 12363-12379. https://doi.org/10.1007/s00521-020-04832-8.
31. Beni, G. (2020). Swarm intelligence. Complex Social and Behavioral Systems: Game Theory and Agent-Based Models, 791-818. https://doi.org/10.1007/978-1-0716-0368-0_530.
32. Prajapati, V. K., Jain, M., & Chouhan, L. (2020, February). Tabu search algorithm (TSA): A comprehensive survey. In 2020 3rd International Conference on Emerging Technologies in Computer Engineering: Machine Learning and Internet of Things (ICETCE) (pp. 1-8). IEEE. https://doi.org/10.1109/ICETCE48199.2020.9091743.
33. Selvakumar, B., & Muneeswaran, K. (2019). Firefly algorithm based feature selection for network intrusion detection. Computers & Security, 81, 71-155. https://doi.org/10.1016/j.cose.2018.11.005
34. Zhou, Y., Cheng, G., Jiang, S., & Dai, M. (2020). Building an efficient intrusion detection system based on feature selection and ensemble classifier. Computer networks, 174, 107247. https://doi.org/10.1016/j.comnet.2020.107247
35. 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
36. Nazir, A., & Khan, R. A. (2021). A novel combinatorial optimization based feature selection method for network intrusion detection. Computers & Security, 102, 102164. https://doi.org/10.1016/j.cose.2020.102164
37. Rani, B. S., Vairamuthu, S., & Subramanian, S. (2024). Archimedes Fire Hawk Optimization Enabled Feature Selection with Deep Maxout for Network Intrusion Detection. Computers & Security, 103751. https://doi.org/10.1016/j.cose.2024.103751
38. Turukmane, A. V., & Devendiran, R. (2024). M-MultiSVM: An efficient feature selection assisted network intrusion detection system using machine learning. Computers & Security, 137, 103587. https://doi.org/10.1016/j.cose.2023.103587
39. Aljehane, N. O., Mengash, H. A., Hassine, S. B., Alotaibi, F. A., Salama, A. S., & Abdelbagi, S. (2024). Optimizing intrusion detection using intelligent feature selection with machine learning model. Alexandria Engineering Journal, 91, 39-49. https://doi.org/10.1016/j.aej.2024.01.073
40. Abualigah, L., Ahmed, S. H., Almomani, M. H., Zitar, R. A., Alsoud, A. R., Abuhaija, B., ... & Elaziz, M. A. (2024). Modified Aquila Optimizer Feature Selection Approach and Support Vector Machine Classifier for Intrusion Detection System. Multimedia Tools and Applications, 1-27. https://doi.org/10.1007/s11042-023-17886-2
41. Jayalatchumy, D., Ramalingam, R., Balakrishnan, A., Safran, M., & Alfarhood, S. (2024). Improved Crow Search-based Feature Selection and Ensemble Learning for IoT Intrusion Detection. IEEE Access. https://doi.org/10.1109/ACCESS.2024.3372859
42. Jiang, T., Fu, X., & Wang, M. (2024). BBO-CFAT: Network intrusion detection model based on BBO algorithm and hierarchical transformer. IEEE Access. https://doi.org/10.1109/ACCESS.2024.3386405
43. Sangaiah, A. K., Javadpour, A., Ja’fari, F., Pinto, P., Zhang, W., & Balasubramanian, S. (2023). A hybrid heuristics artificial intelligence feature selection for intrusion detection classifiers in cloud of things. Cluster Computing, 26(1), 599-612. https://doi.org /10.1007/s10586-022-03629-9.
44. Ye, Z., Luo, J., Zhou, W., Wang, M., & He, Q. (2023). An ensemble framework with improved hybrid breeding optimization-based feature selection for intrusion detection. Future Generation Computer Systems. https://doi.org/10.1016/j.future.2023.09.035
45. Subramani, S., & Selvi, M. (2023). Multi-objective PSO based feature selection for intrusion detection in IoT based wireless sensor networks. Optik, 273, 170419. https://doi.org/10.1016/j.ijleo.2022.170419
46. Alghanam, O. A., Almobaideen, W., Saadeh, M., & Adwan, O. (2023). An improved PIO feature selection algorithm for IoT network intrusion detection system based on ensemble learning. Expert Systems with Applications, 213, 118745. https://doi.org/10.1016/j.eswa.2022.118745
47. Barhoush, M., Abed-alguni, B. H., & Al-qudah, N. E. A. (2023). Improved discrete salp swarm algorithm using exploration and exploitation techniques for feature selection in intrusion detection systems. The Journal of Supercomputing, 79(18), 21265-21309. https://doi.org/10.1007/s11227-023-05444-4
48. Faris, M., Mahmud, M. N., Salleh, M. F. M., & Alsharaa, B. (2023). A differential evolution-based algorithm with maturity extension for feature selection in intrusion detection system. Alexandria Engineering Journal, 81, 178-192. https://doi.org/10.1016/j.aej.2023.09.032
49. Maheswari, K. G., Siva, C., & Nalinipriya, G. (2023). Optimal cluster based feature selection for intrusion detection system in web and cloud computing environment using hybrid teacher learning optimization enables deep recurrent neural network. Computer Communications, 202, 145-153. https://doi.org/10.1016/j.comcom.2023.02.003
50. Rabash, A. J., Nazri, M. Z. A., Shapii, A., & Hasan, M. K. (2023). Non-Dominated Sorting Genetic Algorithm based Dynamic Feature Selection for Intrusion Detection System. IEEE Access. https://doi.org/10.1109/ACCESS.2023.3328395
51. Mohy-Eddine, M., Guezzaz, A., Benkirane, S., & Azrour, M. (2023). An efficient network intrusion detection model for IoT security using K-NN classifier and feature selection. Multimedia Tools and Applications, 82(15), 23615-23633. https://doi.org/10.1007/s11042-023-14795-2
52. Quincozes, S. E., Passos, D., Albuquerque, C., Mossé, D., & Ochi, L. S. (2022). An extended assessment of metaheuristics-based feature selection for intrusion detection in CPS perception layer. Annals of Telecommunications, 77(7), 457-471. https://doi.org/10.1007/s12243-022-00912-z.
53. Saheed, Y. K. (2022). A binary firefly algorithm-based feature selection method on high dimensional intrusion detection data. In Illumination of Artificial Intelligence in Cybersecurity and Forensics (pp. 273-288). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-93453-8_12.
54. Balogun, B. F., Gbolagade, K. A., Arowolo, M. O., & Saheed, Y. K. (2021). A hybrid metaheuristic algorithm for features dimensionality reduction in network intrusion detection system. In Computational Science and Its Applications–ICCSA 2021: 21st International Conference, Cagliari, Italy, September 13–16, 2021, Proceedings, Part IX 21 (pp. 101-114). Springer International Publishing. https://doi.org/10.1007/978-3-030-87013-3_8.
55. Sharma, M., Saini, S., Bahl, S., Goyal, R., & Deswal, S. (2021). Modified bio-inspired algorithms for intrusion detection system. In International Conference on Innovative Computing and Communications: Proceedings of ICICC 2020, Volume 1 (pp. 185-201). Springer Singapore. https://doi.org /10.1007/978-981-15-5113-0_14 .
56. Stankovic, M., Antonijevic, M., Bacanin, N., Zivkovic, M., Tanaskovic, M., & Jovanovic, D. (2022, October). Feature selection by hybrid artificial bee colony algorithm for intrusion detection. In 2022 International Conference on Edge Computing and Applications (ICECAA) (pp. 500-505). IEEE. https://doi.org/10.1109/ICECAA55415.2022.9936116
57. Panigrahi, R., Borah, S., Pramanik, M., Bhoi, A. K., Barsocchi, P., Nayak, S. R., & Alnumay, W. (2022). Intrusion detection in cyber–physical environment using hybrid Naïve Bayes—Decision table and multi-objective evolutionary feature selection. Computer Communications, 188, 133-144. https://doi.org/10.1016/j.comcom.2022.03.009.
58. Ghanem, W. A. H., Ghaleb, S. A. A., Jantan, A., Nasser, A. B., Saleh, S. A. M., Ngah, A., ... & Abiodun, O. I. (2022). Cyber intrusion detection system based on a multiobjective binary bat algorithm for feature selection and enhanced bat algorithm for parameter optimization in neural networks. IEEE Access, 10, 76318-76339. https://doi.org/10.1109/ACCESS.2022.3192472.
59. Aksu, D., & Aydin, M. A. (2022). MGA-IDS: Optimal feature subset selection for anomaly detection framework on in-vehicle networks-CAN bus based on genetic algorithm and intrusion detection approach. Computers & Security, 118, 102717. https://doi.org/10.1016/j.cose.2022.102717
60. Zhao, R., Mu, Y., Zou, L., & Wen, X. (2022). A hybrid intrusion detection system based on feature selection and weighted stacking classifier. IEEE Access, 10, 71414-71426. https://doi.org/10.1109/ACCESS.2022.3186975.
61. Chen, J., Qi, X., Chen, L., Chen, F., & Cheng, G. (2020). Quantum-inspired ant lion optimized hybrid k-means for cluster analysis and intrusion detection. Knowledge-Based Systems, 203, 106167. https://doi.org/10.1016/j.knosys.2020.106167.
62. Kunhare, N., Tiwari, R., & Dhar, J. (2022). Intrusion detection system using hybrid classifiers with meta-heuristic algorithms for the optimization and feature selection by genetic algorithm. Computers and Electrical Engineering, 103, 108383. https://doi.org/10.1016/j.compeleceng.2022.108383.
63. Khanna, A., Rani, P., Garg, P., Singh, P. K., & Khamparia, A. (2022). An enhanced crow search inspired feature selection technique for intrusion detection based wireless network system. Wireless Personal Communications, 127(3), 2021-2038. https://doi.org/10.1007/s11277-021-08766-9.
64. Halim, Z., Yousaf, M. N., Waqas, M., Sulaiman, M., Abbas, G., Hussain, M., ... & Hanif, M. (2021). An effective genetic algorithm-based feature selection method for intrusion detection systems. Computers & Security, 110, 102448. https://doi.org/10.1016/j.cose.2021.102448.
65. Almasoudy, F. H., Al-Yaseen, W. L., & Idrees, A. K. (2020). Differential evolution wrapper feature selection for intrusion detection system. Procedia Computer Science, 167, 1230-1239. https://doi.org/10.1016/j.procs.2020.03.438.
66. Khammassi, C., & Krichen, S. (2020). A NSGA2-LR wrapper approach for feature selection in network intrusion detection. Computer Networks, 172, 107183. https://doi.org/10.1016/j.comnet.2020.107183
67. Elmasry, W., Akbulut, A., & Zaim, A. H. (2020). Evolving deep learning architectures for network intrusion detection using a double PSO metaheuristic. Computer Networks, 168, 107042. https://doi.org/10.1016/j.comnet.2019.107042
68. Ogundokun, R. O., Awotunde, J. B., Sadiku, P., Adeniyi, E. A., Abiodun, M., & Dauda, O. I. (2021). An enhanced intrusion detection system using particle swarm optimization feature extraction technique. Procedia Computer Science, 193, 504-512. https://doi.org/10.1016/j.procs.2021.10.052
69. Velliangiri, S., & Karthikeyan, P. (2020). Hybrid optimization scheme for intrusion detection using considerable feature selection. Neural Computing and Applications, 32(12), 7925-7939. https://doi.org/10.1007/s00521-019-04477-2
70. Alazzam, H., Sharieh, A., & Sabri, K. E. (2020). A feature selection algorithm for intrusion detection system based on pigeon inspired optimizer. Expert systems with applications, 148, 113249. https://doi.org/10.1016/j.eswa.2020.113249
71. Maheswari, S., & Arunesh, K. (2020, September). Unsupervised Binary BAT algorithm based Network Intrusion Detection System using enhanced multiple classifiers. In 2020 International Conference on Smart Electronics and Communication (ICOSEC) (pp. 885-889). IEEE. https://doi.org/10.1109/ICOSEC49089.2020.9215453
72. Vijayanand, R., & Devaraj, D. (2020). A novel feature selection method using whale optimization algorithm and genetic operators for intrusion detection system in wireless mesh network. IEEE Access, 8, 56847-56854. https://doi.org/10.1109/ACCESS.2020.2978035
73. Ravindranath, V., Ramasamy, S., Somula, R., Sahoo, K. S., & Gandomi, A. H. (2020, July). Swarm intelligence based feature selection for intrusion and detection system in cloud infrastructure. In 2020 IEEE congress on evolutionary computation (CEC) (pp. 1-6). IEEE. https://doi.org/10.1109/CEC48606.2020.9185887
74. Al Ogaili, R. R. N., Alomari, E. S., Alkorani, M. B. M., Alyasseri, Z. A. A., Mohammed, M. A., Dhanaraj, R. K., ... & Karuppayah, S. (2023). Malware cyberattacks detection using a novel feature selection method based on a modified whale optimization algorithm. Wireless Networks, 1-17. https://doi.org/10.1007/s11276-023-03606-z
75. Alzubi, Q. M., Anbar, M., Alqattan, Z. N., Al-Betar, M. A., & Abdullah, R. (2020). Intrusion detection system based on a modified binary grey wolf optimisation. Neural computing and applications, 32, 6125-6137. https://doi.org/10.1007/s00521-019-04103-1.
76. Alamiedy, T. A., Anbar, M., Alqattan, Z. N., & Alzubi, Q. M. (2020). Anomaly-based intrusion detection system using multi-objective grey wolf optimisation algorithm. Journal of Ambient Intelligence and Humanized Computing, 11(9), 3735-3756. https://doi.org /10.1007/s12652-019-01569-8.
