[
Alazzam, H., Sharieh, A. and Sabri, K.E. (2020). A feature selection algorithm for intrusion detection system based on pigeon inspired optimizer, Expert Systems with Applications 148: 113249.
]Search in Google Scholar
[
Alzubi, Q.M., Anbar, M., Alqattan, Z.N., Al-Betar, M.A. and Abdullah, R. (2020). Intrusion detection system based on a modified binary grey wolf optimisation, Neural Computing and Applications 32(10): 6125–6137.
]Search in Google Scholar
[
Ashraf, E., Areed, N.F., Salem, H., Abdelhay, E.H. and Farouk, A. (2022). FIDChain: Federated intrusion detection system for blockchain-enabled IoT healthcare applications, Healthcare 10(6): 1110.
]Search in Google Scholar
[
Band, S.S., Janizadeh, S., ChandraPal, S., Saha, A., Chakrabortty, R., Shokri, M. and Mosavi, A. (2020). Novel ensemble approach of deep learning neural network (DLNN) model and particle swarm optimization (PSO) algorithm for prediction of gully erosion susceptibility, Sensors 20(19): 5609.
]Search in Google Scholar
[
Bhattacharjya, A. (2022). A holistic study on the use of blockchain technology in CPS and IoT architectures maintaining the CIA triad in data communication, International Journal of Applied Mathematics and Computer Science 32(3): 403–413, DOI: 10.34768/amcs-2022-0029.
]Search in Google Scholar
[
Carvalho, M. and Ludermir, T.B. (2007). Particle swarm optimization of neural network architectures and weights, 7th International Conference on Hybrid Intelligent Systems (HIS 2007), Kaiserslautern, Germany, pp. 336–339.
]Search in Google Scholar
[
Chen, L., Li, Y., Deng, X., Liu, Z., Lv, M. and Zhang, H. (2022). Dual auto-encoder GAN-based anomaly detection for industrial control system, Applied Sciences 12(10): 4986.
]Search in Google Scholar
[
Chopra, N., Kumar, G. and Mehta, S. (2016). Hybrid GWO-PSO algorithm for solving convex economic load dispatch problem, International Journal of Research in Advent Technology 4(6): 37–41.
]Search in Google Scholar
[
Gao, X., Shan, C., Hu, C., Niu, Z. and Liu, Z. (2019). An adaptive ensemble machine learning model for intrusion detection, IEEE Access 7: 82512–82521.
]Search in Google Scholar
[
Gu, J. and Lu, S. (2021). An effective intrusion detection approach using SVM with na¨ıve Bayes feature embedding, Computers & Security 103: 102158.
]Search in Google Scholar
[
Gu, J., Wang, L., Wang, H. and Wang, S. (2019). A novel approach to intrusion detection using SVM ensemble with feature augmentation, Computers & Security 86: 53–62.
]Search in Google Scholar
[
Hasan, M., Islam, M.M., Zarif, M.I. and Hashem, M.M. (2019). Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches, Internet of Things 7: 100059.
]Search in Google Scholar
[
Huma, Z.E., Latif, S., Ahmad, J., Idrees, Z., Ibrar, A., Zou, Z., Alqahtani, F. and Baothman, F. (2021). A hybrid deep random neural network for cyberattack detection in the industrial Internet of Things, IEEE Access 9: 55595–605.
]Search in Google Scholar
[
Hur, J.H., Ihm, S.Y. and Park, Y.H. (2017). A variable impacts measurement in random forest for mobile cloud computing, Wireless Communications and Mobile Computing 2017, Article ID: 6817627.
]Search in Google Scholar
[
Kennedy, J. and Eberhart, R.C. (1997). A discrete binary version of the particle swarm algorithm, 1997 IEEE International conference on Systems, Man, and Cybernetics, Orlando, USA, pp. 4104–4108.
]Search in Google Scholar
[
Khraisat, A., Gondal, I., Vamplew, P., Kamruzzaman, J. and Alazab, A. (2019). A novel ensemble of hybrid intrusion detection system for detecting Internet of Things attacks, Electronics 8(11): 1210.
]Search in Google Scholar
[
Kim, D. and Heo, T.Y. (2022). Anomaly detection with feature extraction based on machine learning using hydraulic system IoT sensor data, Sensors 22(7): 2479.
]Search in Google Scholar
[
Koroniotis, N., Moustafa, N., Sitnikova, E. and Turnbull, B. (2019). Towards the development of realistic botnet dataset in the Internet of Things for network forensic analytics: BOT-IOT dataset, Future Generation Computer Systems 100: 779–796.
]Search in Google Scholar
[
Kowalski, P.A. and Słoczyński, T. (2021). A modified particle swarm optimization procedure for triggering fuzzy flip-flop neural networks, International Journal of Applied Mathematics and Computer Science 31(4): 577–586, DOI: 10.34768/amcs-2021-0039.
]Search in Google Scholar
[
Kumar, P., Gupta, G.P. and Tripathi, R. (2021a). A distributed ensemble design based intrusion detection system using fog computing to protect the Internet of Things networks, Journal of Ambient Intelligence and Humanized Computing 12(10): 9555–9572.
]Search in Google Scholar
[
Kumar, P., Gupta, G. and Tripathi, R. (2021b). Toward design of an intelligent cyber attack detection system using hybrid feature reduced approach for IoT networks, Arabian Journal for Science and Engineering 46(4): 3749–3778.
]Search in Google Scholar
[
Kunhare, N., Tiwari, R. and Dhar, J. (2020). Particle swarm optimization and feature selection for intrusion detection system, Sādhanā 45(1): 1–14.
]Search in Google Scholar
[
Kusy, M. and Zajdel, R. (2021). A weighted wrapper approach to feature selection, International Journal of Applied Mathematics and Computer Science 31(4): 685–696, DOI: 10.34768/amcs-2021-0047.
]Search in Google Scholar
[
Mahfouz, A.M., Venugopal, D. and Shiva, S.G. (2020). Comparative analysis of ML classifiers for network intrusion detection, 2020 4th International Congress on Information and Communication Technology, London, UK, pp. 193–207.
]Search in Google Scholar
[
Mirjalili, S., Mirjalili, S.M. and Lewis, A. (2014). Grey wolf optimizer, Advances in Engineering Software 69: 46–61.
]Search in Google Scholar
[
Pahl, M. and Aubet, F. (2018). All eyes on you: Distributed multidimensional IoT microservice anomaly detection, 14th International Conference on Network and Service Management (CNSM), Rome, Italy, pp. 72–80.
]Search in Google Scholar
[
Pajouh, H.H., Dastghaibyfard, G. and Hashemi, S. (2017). Two-tier network anomaly detection model: A machine learning approach, Journal of Intelligent Information Systems 48(1): 61–74.
]Search in Google Scholar
[
Safaldin, M., Otair, M. and Abualigah, L. (2021). Improved binary gray wolf optimizer and SVM for intrusion detection system in wireless sensor networks, Journal of Ambient Intelligence and Humanized Computing 12(2): 1559–1576.
]Search in Google Scholar
[
Shafiq, M., Tian, Z., Sun, Y., Du, X. and Guizani, M. (2020). Selection of effective machine learning algorithm and BoT-IoT attacks traffic identification for Internet of Things in smart city, Future Generation Computer Systems 107: 433–442.
]Search in Google Scholar
[
Shafiq, U., Shahzad, M.K., Anwar, M., Shaheen, Q., Shiraz, M. and Gani, A. (2022). Transfer learning auto-encoder neural networks for anomaly detection of DDoS generating IoT devices, Security and Communication Networks 2022, Article ID: 8221351.
]Search in Google Scholar
[
Singh, N. and Singh, S.B. (2017). Hybrid algorithm of particle swarm optimization and grey wolf optimizer for improving convergence performance, Journal of Applied Mathematics 2017, Article ID: 2030489.
]Search in Google Scholar
[
Siwek, K. and Osowski, S. (2016). Data mining methods for prediction of air pollution, International Journal of Applied Mathematics and Computer Science 26(2): 467–478, DOI: 10.1515/amcs-2016-0033.
]Search in Google Scholar
[
Soe, Y., Feng, Y., Santosa, P., Hartanto, R. and Sakurai, K. (2020). Towards a lightweight detection system for cyber attacks in the IoT environment using corresponding features, Electronics 9(1): 144.
]Search in Google Scholar
[
Su, T., Sun, H., Zhu, J., Wang, S. and Li, Y. (2020). BAT: Deep learning methods on network intrusion detection using NSL-KDD dataset, IEEE Access 8: 29575–29585.
]Search in Google Scholar
[
Tavallaee, M., Bagheri, E., Lu, W. and Ghorbani, A.A. (2009). A detailed analysis of the KDD CUP 99 data set, 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications, Ottawa, Canada, pp. 1–6.
]Search in Google Scholar
[
Tian, D. (2018). Particle swarm optimization with chaos-based initialization for numerical optimization, Intelligent Automation & Soft Computing 24(2): 331–342.
]Search in Google Scholar
[
Tian, Q., Han, D., Li, K.C., Liu, X., Duan, L. and Castiglione, A. (2020). An intrusion detection approach based on improved deep belief network, Applied Intelligence 50(10): 3162–3178.
]Search in Google Scholar
[
Wei, W., Chen, S., Lin, Q., Ji, J. and Chen, J. (2020). A multi-objective immune algorithm for intrusion feature selection, Applied Soft Computing 95: 106522.
]Search in Google Scholar
[
Zeeshan, M., Riaz, Q., Bilal, M.A., Shahzad, M.K., Jabeen, H., Haider, S.A. and Rahim, A. (2021). Protocol-based deep intrusion detection for DoS and DDoS attacks using UNSW-NB15 and BoT-IoT data-sets, IEEE Access 10: 2269–83.
]Search in Google Scholar
