[1] T. Saranya, S. Sridevi, C. Deisy, Tran Duc Chung, and MKA Ahamed Khan, "Performance analysis of machine learning algorithms in intrusion detection system: A review," Procedia Computer Science, vol. 171, pp. 1251-1260, 2020. DOI:
10.1016/j.procs.2020.04.133.
[2] Lansky, Jan, Saqib Ali, Mokhtar Mohammadi, Mohammed Kamal Majeed, Sarkhel H. Taher Karim, Shima Rashidi, Mehdi Hosseinzadeh, and Amir Masoud Rahmani. "Deep learning-based intrusion detection systems: a systematic review." IEEE Access 9 (2021): 101574-101599, https://doi.org/10.1109/ACCESS.2021.3097247.
[3] Al-Daweri, Muataz Salam, Khairul Akram Zainol Ariffin, Salwani Abdullah, and Mohamad Firham Efendy Md. Senan. "An analysis of the KDD99 and UNSW-NB15 datasets for the intrusion detection system."Symmetry12,no.10(2020): 1666,
https://doi.org/10.3390/sym12101666.
[4] Al-Tashi, Qasem, Helmi Md Rais, Said Jadid Abdulkadir, Seyedali Mirjalili, and Hitham Alhussian. "A review of grey wolf optimizer-based feature selection methods for classification." Evolutionary Machine Learning Techniques: Algorithms and Applications (2020): 273-286, DOI:10.1007/978-981-32-9990-0_13.
[5] Mirjalili, Seyedali, Ibrahim Aljarah, Majdi Mafarja, Ali Asghar Heidari, and Hossam Faris. "Grey wolf optimizer: theory, literature review, and application in computational fluid dynamics problems." Nature-inspired optimizers: Theories, literature reviews and applications (2020): 87-105, https://doi.org/10.1007/978-3-030-12127-3_6.
[6] Hancer, Emrah, Bing Xue, and Mengjie Zhang. "A survey on feature selection approaches for clustering." Artificial Intelligence Review 53 (2020): 4519-4545, https://doi.org/10.1007/s10462-019-09800-w.
[7] Gawlikowski, Jakob, Cedrique Rovile Njieutcheu Tassi, Mohsin Ali, Jongseok Lee, Matthias Humt, Jianxiang Feng, Anna Kruspe et al. "A survey of uncertainty in deep neural networks." arXiv preprint arXiv:2107.03342 (2021), https://doi.org/10.48550/arXiv.2107.03342.
[8] Ding, Shifei, Xinzheng Xu, and Ru Nie. "Extreme learning machine and its applications." Neural Computing and Applications 25 (2014): 549-556, DOI:10.1007/s00521-013-1522-8.
[9] Huang, Guang-Bin, Qin-Yu Zhu, and Chee-Kheong Siew. "Extreme learning machine: theory and applications." Neurocomputing 70, no. 1-3 (2006): 489-501, https://doi.org/10.1016/j.neucom.2005.12.126
[10] Huang, Gao, Guang-Bin Huang, Shiji Song, and Keyou You. "Trends in extreme learning machines: A review." Neural Networks 61, pp. 32-48, 2015. https://doi.org/10.1016/j.neunet.2014.10.001
[11] Wang, Jian, Siyuan Lu, Shui-Hua Wang, and Yu-Dong Zhang. "A review on extreme learning machine." Multimedia Tools and Applications 81, no. 29 (2022): 41611-41660 , https://doi.org/10.1007/s11042-021-11007-7
[12] Schilling, M., Paskarbeit, J., Hoinville, T., Hüffmeier, A., Schneider, A., Schmitz, J., Cruse, H. (Sept. 17 2013). A hexapod walker using a heterarchical structure for action selection. Frontiers in Computational Neuroscience, 7.
doi:
10.3389/fncom.2013.00126
[13] Maldonado, Javier, María Cristina Riff, and Bertrand Neveu. "A review of recent approaches on wrapper feature selection for intrusion detection." Expert Systems with Applications (2022): 116822 , https://doi.org/10.1016/j.eswa.2022.116822.
[14] Mhawi, Doaa N., Ammar Aldallal, and Soukeana Hassan. "Advanced feature-selection-based hybrid ensemble learning algorithms for network intrusion detection systems." Symmetry 14, no. 7 (2022): 1461 , https://doi.org/10.3390/sym14071461.
[15] Kareem, Saif S., Reham R. Mostafa, Fatma A. Hashim, and Hazem M. El-Bakry. "An effective feature selection model using hybrid metaheuristic algorithms for iot intrusion detection." Sensors 22, no. 4 (2022): 1396 , https://doi.org/10.3390/s22041396.
[16]Naseri, Touraj Sattari, and Farhad Soleimanian Gharehchopogh. "A Feature Selection Based on the Farmland Fertility Algorithm for Improved Intrusion Detection Systems." Journal of Network and Systems Management 30, no. 3 (2022): 1-27 , DOI:10.3390/math10152675
[17] Mojtahedi, Amir, Farid Sorouri, Alireza Najafi Souha, Aidin Molazadeh, and Saeedeh Shafaei Mehr. "Feature Selection-based Intrusion Detection System Using Genetic Whale Optimization Algorithm and Sample-based Classification." arXiv preprint arXiv:2201.00584 (2022).
[18] Otair, Mohammed, Osama Talab Ibrahim, Laith Abualigah, Maryam Altalhi, and Putra Sumari. "An enhanced grey wolf optimizer based particle swarm optimizer for intrusion detection system in wireless sensor networks." Wireless Networks 28, no. 2 (2022): 721-744 , DOI:10.1007/s11276-021-02866-x.
[19] Safaldin, Mukaram, Mohammed Otair, and Laith Abualigah. "Improved binary gray wolf optimizer and SVM for intrusion detection system in wireless sensor networks." Journal of ambient intelligence and humanized computing 12, no. 2 (2021): 1559-1576 , DOI:10.1007/s12652-020-02228-z.
[20] Khanna, Ashish, Poonam Rani, Puneet Garg, Prakash Kumar Singh, and Aditya Khamparia. An Enhanced Crow Search Inspired Feature Selection Technique for Intrusion Detection Based Wireless Network System. Wireless Personal Communications (2021): 1-18 , DOI:10.1007/s11277-021-08766-9.
[21] Keserwani, Pankaj Kumar, Mahesh Chandra Govil, Emmanuel S. Pilli, and Prajjval Govil. "A smart anomaly-based intrusion detection system for the Internet of Things (IoT) network using GWO–PSO–RF model." Journal of Reliable Intelligent Environments 7, no. 1 (2021): 3-21 , DOI: https://doi.org/10.58325/ijisct.003.01.0073.
[22] Kan, Xiu, Yixuan Fan, Zhijun Fang, Le Cao, Neal N. Xiong, Dan Yang, and Xuan Li. "A novel IoT network intrusion detection approach based on adaptive particle swarm optimization convolutional neural network." Information Sciences 568 (2021): 147-162 , https://doi.org/10.1016/j.ins.2021.03.060.
[23] Halim, Zahid, Muhammad Nadeem Yousaf, Muhammad Waqas, Muhammad Sulaiman, Ghulam Abbas, Masroor Hussain, Iftekhar Ahmad, and Muhammad Hanif. "An effective genetic algorithm-based feature selection method for intrusion detection systems." Computers & Security 110 (2021): 102448 , https://doi.org/10.1016/j.cose.2021.102448.
[24] Pradeep Mohan Kumar, K., M. Saravanan, M. Thenmozhi, and K. Vijayakumar. "Intrusion detection system based on GA‐fuzzy classifier for detecting malicious attacks." Concurrency and Computation: Practice and Experience 33, no. 3 (2021): e5242 , https://doi.org/10.1002/cpe.5242.
[25] Ghosh, Partha, Dipankar Sarkar, Joy Sharma, and Santanu Phadikar. "An intrusion detection system using modified-firefly algorithm in cloud environment." International Journal of Digital Crime and Forensics (IJDCF) 13, no. 2 (2021): 77-93 , DOI:10.4018/IJDCF.2021030105.
[26] Alkahtani, Hasan, and Theyazn HH Aldhyani. "Intrusion detection system to advance internet of things infrastructure-based deep learning algorithms." Complexity 2021 (2021) , DOI:10.1155/2021/5579851.
[27] Chen, Jichao, Yijie Zeng, Yue Li, and Guang-Bin Huang. "Unsupervised feature selection based extreme learning machine for clustering." Neurocomputing 386 (2020): 198-207 ,
https://doi.org/10.1016/j.neucom.2019.12.065.
[28] Elsaid, Shaimaa Ahmed, and Nouf Saleh Albatati. "An optimized collaborative intrusion detection system for wireless sensor networks." Soft Computing 24, no. 16 (2020): 12553-12567 ,
https://doi.org/10.1007/s00500-020-04695-0.
[29] همایون, حامد, دهقانی, مهدی، اکبری, حمید، مروری تحلیل ترافیک شبکه گمنامساز پارس با استفاده از یادگیری ماشین. پدافند غیرعامل,، صص 17-1، شماره 12(2)، 1400.
[30] H. M. Saleh, H. Marouane, and A. Fakhfakh, “Stochastic Gradient Descent Intrusions Detection for Wireless Sensor Network Attack Detection System Using Machine Learning,” IEEE Access, vol. 12, no. December 2023, pp. 3825–3836, 2024, doi: 10.1109/ACCESS.2023.3349248.
[31] S. Subramani and M. Selvi, “Intrusion detection system using RBPSO and fuzzy neuro-genetic classification algorithms in wireless sensor networks,” Int. J. Inf. Comput. Secur., vol. 20, no. 3–4, pp. 439–461, 2023, doi: 10.1504/IJICS.2023.128857.
[32] S. Subramani and M. Selvi, “Multi-objective PSO based feature selection for intrusion detection in IoT based wireless sensor networks,” Optik (Stuttg)., vol. 273, no. December 2022, p. 170419, 2023, doi: 10.1016/j.ijleo.2022.170419.
[33] F. Al-Quayed, Z. Ahmad, and M. Humayun, “A Situation Based Predictive Approach for Cybersecurity Intrusion Detection and Prevention Using Machine Learning and Deep Learning Algorithms in Wireless Sensor Networks of Industry 4.0,” IEEE Access, vol. 12, no. February, pp. 34800–34819, 2024, doi: 10.1109/ACCESS.2024.3372187.
[34] M. Aljebreen et al., “Binary Chimp Optimization Algorithm with ML Based Intrusion Detection for Secure IoT-Assisted Wireless Sensor Networks,” Sensors, vol. 23, no. 8, 2023, doi: 10.3390/s23084073.
[35] M. H. Behiry and M. Aly, “Cyberattack detection in wireless sensor networks using a hybrid feature reduction technique with AI and machine learning methods,” J. Big Data, vol. 11, no. 1, 2024, doi: 10.1186/s40537-023-00870-w.
[36] L. Dhanabal and S. P. Shantharajah, “A Study on NSL-KDD Dataset for Intrusion Detection System Based on Classification Algorithms,” Int. J. Adv. Res. Comput. Commun. Eng., vol. 4, no. 6, pp. 446–452, 2015, doi: 10.17148/IJARCCE.2015.4696.
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