A multi-objective mutation-based dynamic Harris Hawks optimization for botnet detection in IoT

Internet of Things - Tập 24 - Trang 100952 - 2023
Farhad Soleimanian Gharehchopogh1, Benyamin Abdollahzadeh2, Saeid Barshandeh3, Bahman Arasteh4
1Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran
2Department of Mathematics, Faculty of Science, University of Hradec Kralove, Rokitanskeho 62, Hradec Kralove 50003, Czech Republic
3Department of Computer Science, School of Engineering, Afagh Higher Education Institute, Urmia, Iran
4Department of Software Engineering, Faculty of Engineering and Natural Science, Istinye University, Istanbul, Turkey

Tài liệu tham khảo

Salim, 2020, Distributed denial of service attacks and its defenses in IoT: a survey, J. Supercomput., 76, 5320, 10.1007/s11227-019-02945-z Kolias, 2017, DDoS in the IoT: Mirai and other botnets, Computer, 50, 80, 10.1109/MC.2017.201 Zhang, 2020, 32 Samadi Bonab, 2020, A wrapper-based feature selection for improving the performance of intrusion detection systems, Int. J. Commun. Syst., e4434, 10.1002/dac.4434 Garcia-Teodoro, 2009, Anomaly-based network intrusion detection: techniques, systems and challenges, Comput. Secur., 28, 18, 10.1016/j.cose.2008.08.003 Nhu, 2021, Fuzzy-based distributed behavioral control with wall-following strategy for swarm navigation in arbitrary-shaped environments, IEEE Access, 9, 139176, 10.1109/ACCESS.2021.3119232 Gharehchopogh, 2020, A comprehensive survey on symbiotic organisms search algorithms, Artif. Intell. Rev., 53, 2265, 10.1007/s10462-019-09733-4 Nadimi-Shahraki, 2022, Binary approaches of quantum-based avian navigation optimizer to select effective features from high-dimensional medical data, Mathematics, 10, 2770, 10.3390/math10152770 Nguyen, 2023, Using real-time operating system to control the recycling waste system in beverage industry for circular economy: mechanical approach, Results Eng., 18, 10.1016/j.rineng.2023.101083 Gharehchopogh, 2023, Cqffa: a chaotic quasi-oppositional farmland fertility algorithm for solving engineering optimization problems, J. Bionic Eng., 20, 158, 10.1007/s42235-022-00255-4 Shayanfar, 2018, Farmland fertility: a new metaheuristic algorithm for solving continuous optimization problems, Appl. Soft Comput., 71, 728, 10.1016/j.asoc.2018.07.033 Nadimi-Shahraki, 2023, An effective hybridization of quantum-based avian navigation and bonobo optimizers to solve numerical and mechanical engineering problems, J. Bionic Eng., 20, 1361, 10.1007/s42235-022-00323-9 Nadimi-Shahraki, 2023, MMKE: multi-trial vector-based monkey king evolution algorithm and its applications for engineering optimization problems, PLoS One, 18, 10.1371/journal.pone.0280006 Habib, 2020, Multi-objective particle swarm optimization for botnet detection in Internet of Things, 203 Asadi, 2020, Detecting botnet by using particle swarm optimization algorithm based on voting system, Fut. Gener. Comput. Syst., 107, 95, 10.1016/j.future.2020.01.055 Lin, 2014, Botnet detection using support vector machines with artificial fish swarm algorithm, J. Appl. Math., 10.1155/2014/986428 Abdollahzadeh, 2021, A multi-objective optimization algorithm for feature selection problems, Eng. Comput., 1 Ghafori, 2022, 177 Nguyen, 2023, Developing and evaluating the context-aware performance of synchronization control in the real-time network protocol for the connected vehicle, Mob. Netw. Appl., 1 Hosseini, 2022, A botnet detection in IoT using a hybrid multi-objective optimization algorithm, New Gener. Comput., 40, 809, 10.1007/s00354-022-00188-w Hosseini, 2023, MOAEOSCA: an enhanced multi-objective hybrid artificial ecosystem-based optimization with sine cosine algorithm for feature selection in botnet detection in IoT, Multimed. Tools Appl., 82, 13369, 10.1007/s11042-022-13836-6 Panigrahi, 2022, Intrusion detection in cyber–physical environment using hybrid Naïve Bayes—decision table and multi-objective evolutionary feature selection, Comput. Commun., 188, 133, 10.1016/j.comcom.2022.03.009 Kiruthika, 2022, Multi-objective fish swarm optimization with fuzzy association rule for botnet detection system Chen, 2022, Intrusion detection using multi-objective evolutionary convolutional neural network for Internet of Things in Fog computing, Knowl. Based Syst., 244, 10.1016/j.knosys.2022.108505 Lee, 2021, Classification of botnet attacks in IoT smart factory using honeypot combined with machine learning, PeerJ Comput. Sci., 7, e350, 10.7717/peerj-cs.350 Téllez, 2018, A tabu search method for load balancing in fog computing, Int. J. Artif. Intell., 16, 1 Rana, 2018, An effective lightweight cryptographic algorithm to secure resource-constrained devices, Int. J. Adv. Comput. Sci. Appl., 9 Habib, 2020, A modified multi-objective particle swarm optimizer-based lévy flight: an approach toward intrusion detection in Internet of Things, Arab. J. Sci. Eng., 45, 6081, 10.1007/s13369-020-04476-9 Xue, 2018 Kesavamoorthy, 2019, Swarm intelligence based autonomous DDoS attack detection and defense using multi agent system, Clust. Comput., 22, 9469, 10.1007/s10586-018-2365-y Li, 2018, Ai-based two-stage intrusion detection for software defined iot networks, IEEE Internet Things J., 6, 2093, 10.1109/JIOT.2018.2883344 Li, 2018, 6, 10311 Al Shorman, 2020, Unsupervised intelligent system based on one class support vector machine and Grey Wolf optimization for IoT botnet detection, J. Ambient Intell. Humaniz. Comput., 11, 2809, 10.1007/s12652-019-01387-y Khan, 2018, IoT security: review, blockchain solutions, and open challenges, Future Gener. Comput. Syst., 82, 395, 10.1016/j.future.2017.11.022 De la Hoz, 2014, Feature selection by multi-objective optimisation: application to network anomaly detection by hierarchical self-organising maps, Knowl. Based Syst., 71, 322, 10.1016/j.knosys.2014.08.013 Wang, 2015, Constructing important features from massive network traffic for lightweight intrusion detection, IET Inf. Secur., 9, 374, 10.1049/iet-ifs.2014.0353 Zhu, 2017, An improved NSGA-III algorithm for feature selection used in intrusion detection, Knowl. Based Syst., 116, 74, 10.1016/j.knosys.2016.10.030 Xue, 2018 McDermott, 2018, Botnet detection in the Internet of Things using deep learning approaches Rana, S., et al., An effective lightweight cryptographic algorithm to secure resource-constrained devices. Spectr., 2018. 9(11). Nguyen, 2018, IoT botnet detection approach based on PSI graph and DGCNN classifier Sanchez-Pi, 2018, Applying voreal for iot intrusion detection Bezerra, V.H., et al. One-class classification to detect botnets in IoT devices∗. in Anais principais do XVIII Simpósio Brasileiro em Segurança da Informação e de Sistemas Computacionais. 2018. SBC. Kesavamoorthy, 2019, Swarm intelligence based autonomous DDoS attack detection and defense using multi agent system, Clust. Comput., 22, 9469, 10.1007/s10586-018-2365-y Selvarani, 2019, Secure and optimal authentication framework for cloud management using HGAPSO algorithm, Clust. Comput., 22, 4007, 10.1007/s10586-018-2609-x Suman, C., Tripathy S., and Saha S., Building an effective intrusion detection system using unsupervised feature selection in multi-objective optimization framework. arXiv preprint arXiv:1905.06562, 2019. Al-Kasassbeh, 2023, Detection of IoT-botnet attacks using fuzzy rule interpolation, J. Intell. Fuzzy Syst., 1 Roopak, 2020, Multi-objective-based feature selection for DDoS attack detection in IoT networks, IET Netw., 9, 120, 10.1049/iet-net.2018.5206 Saleh, 2021, Using monkey optimization algorithm to detect Neris botnet, J. Eng. Sci. Technol., 16, 152 Jagadeesan, 2021, An efficient botnet detection with the enhanced support vector neural network, Measurement, 10.1016/j.measurement.2021.109140 Bagui, 2021, Machine learning based intrusion detection for IoT botnet, Int. J. Mach. Learn. Comput., 11 Deb, 2001, 16 Liu, 2017, A many-objective evolutionary algorithm using a one-by-one selection strategy, IEEE Trans. Cybern., 47, 2689, 10.1109/TCYB.2016.2638902 Heidari, 2019, Harris hawks optimization: algorithm and applications, Future Gener. Comput. Syst., 97, 849, 10.1016/j.future.2019.02.028 Jia, 2019, Dynamic harris hawks optimization with mutation mechanism for satellite image segmentation, Remote. Sens., 11, 1421, 10.3390/rs11121421 Abd Elaziz, 2020, A competitive chain-based Harris Hawks optimizer for global optimization and multi-level image thresholding problems, Appl. Soft Comput. Abbasi, 2019, On the application of Harris hawks optimization (HHO) algorithm to the design of microchannel heat sinks, Eng. Comput., 1 Hussain, 2019, Long-term memory Harris’ hawk optimization for high dimensional and optimal power flow problems, IEEE Access, 7, 147596, 10.1109/ACCESS.2019.2946664 Zhang, 2020, Boosted binary Harris hawks optimizer and feature selection, Structure, 25, 26 Hans, 2020, Opposition-based Harris Hawks optimization algorithm for feature selection in breast mass classification, J. Interdiscip. Math., 23, 97, 10.1080/09720502.2020.1721670 Abdel-Basset, 2020, A hybrid Harris Hawks optimization algorithm with simulated annealing for feature selection, Artif. Intell. Rev., 1 Abd Elaziz, 2019, Task scheduling in cloud computing based on hybrid moth search algorithm and differential evolution, Knowl. Based Syst., 169, 39, 10.1016/j.knosys.2019.01.023 Jadon, 2017, Hybrid artificial bee colony algorithm with differential evolution, Appl. Soft Comput., 58, 11, 10.1016/j.asoc.2017.04.018 Xiong, 2018, Parameter extraction of solar photovoltaic models by means of a hybrid differential evolution with whale optimization algorithm, Sol. Energy, 176, 742, 10.1016/j.solener.2018.10.050 Das, 2016, Recent advances in differential evolution–an updated survey, Swarm Evol. Comput., 27, 1, 10.1016/j.swevo.2016.01.004 Zhang, 2007, MOEA/D: a multiobjective evolutionary algorithm based on decomposition, IEEE Trans. Evol. Comput., 11, 712, 10.1109/TEVC.2007.892759 Coello, 2002, MOPSO: a proposal for multiple objective particle swarm optimization Balachandran, 2012, Optimizing properties of nanoclay–nitrile rubber (NBR) composites using face centred central composite design, Mater. Des., 35, 854, 10.1016/j.matdes.2011.03.077