乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,12,30]],"date-time":"2024-12-30T19:17:39Z","timestamp":1735586259740},"reference-count":83,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T00:00:00Z","timestamp":1704758400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T00:00:00Z","timestamp":1704758400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Institute for Energy Technology"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Inf. Secur."],"published-print":{"date-parts":[[2024,6]]},"abstract":"Abstract<\/jats:title>The Internet of Things (IoT) has garnered considerable attention from academic and industrial circles as a pivotal technology in recent years. The escalation of security risks is observed to be associated with the growing interest in IoT applications. Intrusion detection systems (IDS) have been devised as viable instruments for identifying and averting malicious actions in this context. Several techniques described in academic papers are thought to be very accurate, but they cannot be used in the real world because the datasets used to build and test the models do not accurately reflect and simulate the IoT network. Existing methods, on the other hand, deal with these issues, but they are not good enough for commercial use because of their lack of precision, low detection rate, receiver operating characteristic (ROC), and false acceptance rate (FAR). The effectiveness of these solutions is predominantly dependent on individual learners and is consequently influenced by the inherent limitations of each learning algorithm. This study introduces a new approach for detecting intrusion attacks in an IoT network, which involves the use of an ensemble learning technique based on gray wolf optimizer (GWO). The novelty of this study lies in the proposed voting gray wolf optimizer (GWO) ensemble model, which incorporates two crucial components: a traffic analyzer and a classification phase engine. The model employs a voting technique to combine the probability averages of the base learners. Secondly, the combination of feature selection and feature extraction techniques is to reduce dimensionality. Thirdly, the utilization of GWO is employed to optimize the parameters of ensemble models. Similarly, the approach employs the most authentic intrusion detection datasets that are accessible and amalgamates multiple learners to generate ensemble learners. The hybridization of information gain (IG) and principal component analysis (PCA) was employed to reduce dimensionality. The study utilized a novel GWO ensemble learning approach that incorporated a decision tree, random forest, K-nearest neighbor, and multilayer perceptron for classification. To evaluate the efficacy of the proposed model, two authentic datasets, namely, BoT-IoT and UNSW-NB15, were scrutinized. The GWO-optimized ensemble model demonstrates superior accuracy when compared to other machine learning-based and deep learning models. Specifically, the model achieves an accuracy rate of 99.98%, a DR of 99.97%, a precision rate of 99.94%, an ROC rate of 99.99%, and an FAR rate of 1.30 on the BoT-IoT dataset. According to the experimental results, the proposed ensemble model optimized by GWO achieved an accuracy of 100%, a DR of 99.9%, a precision of 99.59%, an ROC of 99.40%, and an FAR of 1.5 when tested on the UNSW-NB15 dataset.<\/jats:p>","DOI":"10.1007\/s10207-023-00803-x","type":"journal-article","created":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T10:02:58Z","timestamp":1704794578000},"page":"1557-1581","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["A voting gray wolf optimizer-based ensemble learning models for intrusion detection in the Internet of Things"],"prefix":"10.1007","volume":"23","author":[{"given":"Yakub Kayode","family":"Saheed","sequence":"first","affiliation":[]},{"given":"Sanjay","family":"Misra","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,1,9]]},"reference":[{"issue":"2","key":"803_CR1","doi-asserted-by":"publisher","first-page":"1801","DOI":"10.32604\/cmc.2021.018466","volume":"69","author":"N Islam","year":"2021","unstructured":"Islam, N., et al.: Towards Machine learning based intrusion detection in IoT networks. Comput. Mater. Contin. 69(2), 1801\u20131821 (2021). https:\/\/doi.org\/10.32604\/cmc.2021.018466","journal-title":"Comput. Mater. Contin."},{"issue":"2","key":"803_CR2","doi-asserted-by":"publisher","first-page":"8","DOI":"10.3390\/computers8020040","volume":"8","author":"MA Rahman","year":"2019","unstructured":"Rahman, M.A., Asyhari, A.T.: The emergence of Internet of things (IoT): connecting anything, anywhere. Computers 8(2), 8\u201311 (2019). https:\/\/doi.org\/10.3390\/computers8020040","journal-title":"Computers"},{"issue":"10","key":"803_CR3","doi-asserted-by":"publisher","first-page":"7114","DOI":"10.1109\/TII.2020.3038780","volume":"17","author":"H Lin","year":"2021","unstructured":"Lin, H., Hu, J., Wang, X., Alhamid, M.F., Piran, M.J.: Toward secure data fusion in industrial IoT using transfer learning. IEEE Trans. Ind. Inform. 17(10), 7114\u20137122 (2021). https:\/\/doi.org\/10.1109\/TII.2020.3038780","journal-title":"IEEE Trans. Ind. Inform."},{"key":"803_CR4","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-18732-3","volume-title":"Digital Twin Technologies and Smart Cities","author":"M Farsi","year":"2020","unstructured":"Farsi, M., Daneshkhah, A., Hosseinian-Far, H., Jahankhani, A.: Digital Twin Technologies and Smart Cities. Springer, Berlin\/Heidelberg, Germany (2020)"},{"key":"803_CR5","doi-asserted-by":"publisher","unstructured":"Zhao, K., Ge, L.: A survey on the Internet of things security. In: Proceedings\u20149th International Conference on Computational Intelligence and Security, CIS 2013, pp. 663\u2013667 (2013). https:\/\/doi.org\/10.1109\/CIS.2013.145.","DOI":"10.1109\/CIS.2013.145"},{"key":"803_CR6","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1016\/j.jnca.2014.01.014","volume":"42","author":"Z Yan","year":"2014","unstructured":"Yan, Z., Zhang, P., Vasilakos, A.V.: A survey on trust management for Internet of Things. J. Netw. Comput. Appl. 42, 120\u2013134 (2014). https:\/\/doi.org\/10.1016\/j.jnca.2014.01.014","journal-title":"J. Netw. Comput. Appl."},{"issue":"5","key":"803_CR7","first-page":"151","volume":"7","author":"YK Saheed","year":"2014","unstructured":"Saheed, Y.K., Babatunde, A.O.: Genetic algorithm technique in program path coverage for improving software testing. Afr. J. Comput. ICT 7(5), 151\u2013158 (2014)","journal-title":"Afr. J. Comput. ICT"},{"key":"803_CR8","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1016\/j.comnet.2019.01.023","volume":"151","author":"AP Kelton","year":"2019","unstructured":"Kelton, A.P., Papa, J.P., Lisboa, C.O., Munoz, R., De, V.H.C.: Internet of Things: a survey on machine learning-based intrusion detection approaches. Comput. Netw. 151, 147\u2013157 (2019). https:\/\/doi.org\/10.1016\/j.comnet.2019.01.023","journal-title":"Comput. Netw."},{"key":"803_CR9","doi-asserted-by":"publisher","unstructured":"Saheed, Y.K., Misra, S., Chockalingam, S.: Autoencoder via DCNN and LSTM models for intrusion detection in industrial control systems of critical infrastructures. In: 2023 IEEE\/ACM 4th Int. Work. Eng. Cybersecurity Crit. Syst. (EnCyCriS), Melbourne, Aust., pp. 9\u201316 (2023). https:\/\/doi.org\/10.1109\/EnCyCriS59249.2023.00006","DOI":"10.1109\/EnCyCriS59249.2023.00006"},{"key":"803_CR10","doi-asserted-by":"crossref","unstructured":"Alharbi, S., Rodriguez, P., Maharaja, R., Iyer, P., Bose, N., Ye, Z.: FOCUS\u202f: a fog computing-based security system for the Internet of Things. (2018)","DOI":"10.1109\/CCNC.2018.8319238"},{"issue":"2","key":"803_CR11","doi-asserted-by":"publisher","first-page":"314","DOI":"10.1109\/TETC.2016.2633228","volume":"7","author":"HH Pajouh","year":"2019","unstructured":"Pajouh, H.H., Javidan, R., Khayami, R., Dehghantanha, A., Choo, K.K.R.: A two-layer dimension reduction and two-tier classification model for anomaly-based intrusion detection in IoT backbone networks. IEEE Trans. Emerg. Top. Comput. 7(2), 314\u2013323 (2019). https:\/\/doi.org\/10.1109\/TETC.2016.2633228","journal-title":"IEEE Trans. Emerg. Top. Comput."},{"key":"803_CR12","doi-asserted-by":"crossref","unstructured":"Tavallaee, M., Bagheri, E., Lu, W., Ghorbani, A.A.: A detailed analysis of the KDD CUP 99 data set. no. Cisda, pp. 1\u20136 (2009).","DOI":"10.1109\/CISDA.2009.5356528"},{"key":"803_CR13","doi-asserted-by":"crossref","unstructured":"Zhang, H., Wu, C.Q., Gao, S., Wang, Z., Xu, Y., Liu, Y.: An effective deep learning based scheme for network intrusion detection. In: 2018 24th Int. Conf. Pattern Recognit., pp. 682\u2013687 (2018)","DOI":"10.1109\/ICPR.2018.8546162"},{"key":"803_CR14","doi-asserted-by":"publisher","unstructured":"Moustafa, N., Slay, J.: UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set). In: 2015 Mil. Commun. Inf. Syst. Conf. MilCIS 2015\u2014Proc. (2015). https:\/\/doi.org\/10.1109\/MilCIS.2015.7348942","DOI":"10.1109\/MilCIS.2015.7348942"},{"key":"803_CR15","unstructured":"Koroniotis, N., Moustafa, N., Sitnikova, E.: Towards Developing Network Forensic Mechanism for Botnet Activities in the IoT Based on Machine Learning Techniques. Springer International Publishing"},{"key":"803_CR16","doi-asserted-by":"publisher","unstructured":"Kolias, C., Kambourakis, G., Stavrou, A., Gritzalis, S.: Intrusion detection in 802. 11 Networks\u202f: Empirical Evaluation of Threats and a Public Dataset. no. c, pp. 1\u201324 (2015). https:\/\/doi.org\/10.1109\/COMST.2015.2402161","DOI":"10.1109\/COMST.2015.2402161"},{"key":"803_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3389\/fcomp.2023.997159","volume":"5","author":"YK Saheed","year":"2023","unstructured":"Saheed, Y.K., Usman, A.A., Sukat, F.D., Abdulrahman, M.: A novel hybrid autoencoder and modified particle swarm optimization feature selection for intrusion detection in the internet of things network. Front. Comput. Sci. 5, 1\u201313 (2023). https:\/\/doi.org\/10.3389\/fcomp.2023.997159","journal-title":"Front. Comput. Sci."},{"key":"803_CR18","doi-asserted-by":"publisher","unstructured":"Amin, S.O., Siddiqui, M.S., Hong, C.S., Choe, J.: A novel coding scheme to implement signature based IDS in IP based sensor networks. In: 2009 IFIP\/IEEE Int. Symp. Integr. Netw. Manag. IM 2009, pp. 269\u2013274 (2009).https:\/\/doi.org\/10.1109\/INMW.2009.5195973","DOI":"10.1109\/INMW.2009.5195973"},{"key":"803_CR19","doi-asserted-by":"crossref","unstructured":"Abubakar, A., Pranggono, B.: Machine learning based intrusion detection system for software defined networks. In: 2017 Seventh International Conference on Emerging Security Technologies, pp. 138\u2013143 (2017)","DOI":"10.1109\/EST.2017.8090413"},{"key":"803_CR20","doi-asserted-by":"publisher","unstructured":"Roy, B., Cheung, H.: A deep learning approach for intrusion detection in internet of things using bi-directional long short-term memory recurrent neural network. In: 2018 28th Int. Telecommun. Networks Appl. Conf. ITNAC 2018, pp. 1\u20136 (2019).https:\/\/doi.org\/10.1109\/ATNAC.2018.8615294","DOI":"10.1109\/ATNAC.2018.8615294"},{"issue":"1","key":"803_CR21","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/WD.2011.6098218","volume":"1","author":"A Le","year":"2011","unstructured":"Le, A., Loo, J., Luo, Y., Lasebae, A.: Specification-based IDS for securing RPL from topology attacks. IFIP Wirel. Days 1(1), 4\u20136 (2011). https:\/\/doi.org\/10.1109\/WD.2011.6098218","journal-title":"IFIP Wirel. Days"},{"key":"803_CR22","doi-asserted-by":"crossref","unstructured":"Bertino, E.: Botnets and Internet of Things Security. Computer (Long. Beach. Calif)., pp. 76\u201379 (2017)","DOI":"10.1109\/MC.2017.62"},{"key":"803_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.simpat.2019.102031","volume":"101","author":"M Almiani","year":"2020","unstructured":"Almiani, M., AbuGhazleh, A., Al-Rahayfeh, A., Atiewi, S., Razaque, A.: Deep recurrent neural network for IoT intrusion detection system. Simul. Model. Pract. Theory 101, 102031 (2020). https:\/\/doi.org\/10.1016\/j.simpat.2019.102031","journal-title":"Simul. Model. Pract. Theory"},{"key":"803_CR24","doi-asserted-by":"publisher","unstructured":"Li, Z., Batta, P., Trajkovi, L.: Comparison of Machine Learning Algorithms for Detection of Network Intrusions. pp. 4248\u20134253 (2018). https:\/\/doi.org\/10.1109\/SMC.2018.00719","DOI":"10.1109\/SMC.2018.00719"},{"key":"803_CR25","doi-asserted-by":"crossref","unstructured":"Ayyaz-ul-haq, Q., Larijani, H., Ahmad, J.: A heuristic intrusion detection system for Internet-of-Things (IoT). In: Arai, K., Bhatia, R., Kapoor, S. (eds.) Intelligent Computing. CompCom 2019. Advances in Intelligent Systems and Computing. Springer Cham, pp. 86\u201398 (2019)","DOI":"10.1007\/978-3-030-22871-2_7"},{"key":"803_CR26","doi-asserted-by":"publisher","unstructured":"B\u00f6hm, A., Jonsson, M., Uhlemann, E.: Performance comparison of a platooning application using the IEEE 802.11p MAC on the control channel and a centralized MAC on a service channel. Int. Conf. Wirel. Mob. Comput. Netw. Commun. 545\u2013552 (2013).https:\/\/doi.org\/10.1109\/WiMOB.2013.6673411","DOI":"10.1109\/WiMOB.2013.6673411"},{"issue":"4","key":"803_CR27","doi-asserted-by":"publisher","first-page":"919","DOI":"10.1111\/coin.12220","volume":"35","author":"W Elmasry","year":"2019","unstructured":"Elmasry, W., Akbulut, A., Zaim, A.H.: Empirical study on multiclass classification-based network intrusion detection. Comput. Intell. 35(4), 919\u2013954 (2019). https:\/\/doi.org\/10.1111\/coin.12220","journal-title":"Comput. Intell."},{"issue":"3","key":"803_CR28","doi-asserted-by":"publisher","first-page":"32464","DOI":"10.1109\/ACCESS.2020.2973730","volume":"8","author":"K Jiang","year":"2020","unstructured":"Jiang, K., Wang, W., Wang, A., Wu, H.: Network intrusion detection combined hybrid sampling with deep hierarchical network. IEEE Access 8(3), 32464\u201332476 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.2973730","journal-title":"IEEE Access"},{"key":"803_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.iot.2019.100059","volume":"7","author":"M Hasan","year":"2019","unstructured":"Hasan, M., Islam, M., Zarif, I.I., Hashem, M.M.A.: Internet of things attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches. Internet Things 7, 100059 (2019). https:\/\/doi.org\/10.1016\/j.iot.2019.100059","journal-title":"Internet Things"},{"key":"803_CR30","doi-asserted-by":"publisher","unstructured":"Cheng, Y., Xu, Y., Zhong, H., Liu, Y.: Leveraging Semi-supervised Hierarchical Stacking Temporal Convolutional Network for Anomaly Detection in IoT Communication, vol. 4662, no. c (2020). https:\/\/doi.org\/10.1109\/JIOT.2020.3000771.","DOI":"10.1109\/JIOT.2020.3000771"},{"key":"803_CR31","doi-asserted-by":"publisher","unstructured":"Lee, T.H., Wen, C.H., Chang, L.H., Chiang, H.S., Hsieh, M.C.: A lightweight intrusion detection scheme based on energy consumption analysis in 6LowPAN. In: Advanced Technologies, Embedded and Multimedia for Human-centric Computing (2014). https:\/\/doi.org\/10.1007\/978-94-007-7262-5_137","DOI":"10.1007\/978-94-007-7262-5_137"},{"key":"803_CR32","doi-asserted-by":"publisher","unstructured":"Sahu, N.K., Mukherjee, I.: Machine learning based anomaly detection for IoT network:(Anomaly detection in IoT network). In: 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184), no. Icoei, pp. 787\u2013794 (2020). https:\/\/doi.org\/10.1109\/ICOEI48184.2020.9142921","DOI":"10.1109\/ICOEI48184.2020.9142921"},{"key":"803_CR33","doi-asserted-by":"publisher","unstructured":"Chen, J., Chen, C.: Design of complex event-processing IDS in internet of things. In: Proc. - 2014 6th Int. Conf. Meas. Technol. Mechatronics Autom. ICMTMA 2014, pp. 226\u2013229 (2014). https:\/\/doi.org\/10.1109\/ICMTMA.2014.57","DOI":"10.1109\/ICMTMA.2014.57"},{"key":"803_CR34","doi-asserted-by":"publisher","unstructured":"Midi, D., Rullo, A., Mudgerikar, A., Bertino, E.: Kalis\u2014a system for knowledge-driven adaptable intrusion detection for the Internet of Things. In: Proc. - Int. Conf. Distrib. Comput. Syst., pp. 656\u2013666 (2017). https:\/\/doi.org\/10.1109\/ICDCS.2017.104","DOI":"10.1109\/ICDCS.2017.104"},{"key":"803_CR35","doi-asserted-by":"publisher","unstructured":"Karunkumar, D., Himansu, R., Behera, S., Nayak, J.: Deep neural network based anomaly detection in Internet of Things network traffic tracking for the applications of future smart cities. no. July, pp. 1\u201326 (2020). https:\/\/doi.org\/10.1002\/ett.4121","DOI":"10.1002\/ett.4121"},{"key":"803_CR36","doi-asserted-by":"publisher","DOI":"10.3390\/s17091967","author":"M Lopez-Martin","year":"2017","unstructured":"Lopez-Martin, M., Carro, B., Sanchez-Esguevillas, A., Lloret, J.: Conditional variational autoencoder for prediction and feature recovery applied to intrusion detection in iot. Sensors (Switzerland) (2017). https:\/\/doi.org\/10.3390\/s17091967","journal-title":"Sensors (Switzerland)"},{"key":"803_CR37","doi-asserted-by":"crossref","unstructured":"Guller, M.: Big data analytics with Spark: A practitioner\u2019s guide to using Spark for large scale data analysis. Apress (2015)","DOI":"10.1007\/978-1-4842-0964-6"},{"key":"803_CR38","doi-asserted-by":"publisher","unstructured":"Joshi, H.P., Bennison, M., Dutta, R.: Collaborative botnet detection with partial communication graph information. In: 2017 IEEE 38th Sarnoff Symp. (2017). https:\/\/doi.org\/10.1109\/SARNOF.2017.8080397","DOI":"10.1109\/SARNOF.2017.8080397"},{"key":"803_CR39","doi-asserted-by":"publisher","unstructured":"Soe, Y.N., Feng, Y., Santosa, P.I., Hartanto, R., Sakurai, K.: A sequential scheme for detecting cyber attacks in IoT environment. In: Proc. - IEEE 17th Int. Conf. Dependable, Auton. Secur. Comput. IEEE 17th Int. Conf. Pervasive Intell. Comput. IEEE 5th Int. Conf. Cloud Big Data Comput. 4th Cyber Sci., vol. 324, pp. 238\u2013244 (2019). https:\/\/doi.org\/10.1109\/DASC\/PiCom\/CBDCom\/CyberSciTech.2019.00051","DOI":"10.1109\/DASC\/PiCom\/CBDCom\/CyberSciTech.2019.00051"},{"key":"803_CR40","doi-asserted-by":"publisher","unstructured":"Soe, Y.N., Santosa, P.I., Hartanto, R.: DDoS attack detection based on simple ANN with SMOTE for IoT environment. In: Proc. 2019 4th Int. Conf. Informatics Comput. ICIC 2019, pp. 0\u20134 (2019). https:\/\/doi.org\/10.1109\/ICIC47613.2019.8985853","DOI":"10.1109\/ICIC47613.2019.8985853"},{"key":"803_CR41","doi-asserted-by":"crossref","unstructured":"Le, H.V., Ngo, Q.D., Le, V.H.: Iot Botnet detection using system call graphs and one-class CNN classification. Int. J. Innov. Technol. Explor. Eng. 8(10) (2019).","DOI":"10.35940\/ijitee.J9091.0881019"},{"key":"803_CR42","doi-asserted-by":"publisher","unstructured":"Kumar, A., Lim, T.J.: EDIMA: early detection of IoT malware network activity using machine learning techniques. In: IEEE 5th World Forum Internet Things, WF-IoT 2019\u2014Conf. Proc., pp. 289\u2013294 (2019). https:\/\/doi.org\/10.1109\/WF-IoT.2019.8767194","DOI":"10.1109\/WF-IoT.2019.8767194"},{"issue":"c","key":"803_CR43","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/ACCESS.2018.2867564","volume":"PP","author":"C Xu","year":"2018","unstructured":"Xu, C., Member, S., Shen, J., Du, X.I.N., Zhang, F.A.N.: An intrusion detection system using a deep neural network with gated recurrent units. IEEE Access PP(c), 1 (2018). https:\/\/doi.org\/10.1109\/ACCESS.2018.2867564","journal-title":"IEEE Access"},{"key":"803_CR44","doi-asserted-by":"publisher","unstructured":"Chaudhary, P., Gupta, B.B.: DDoS detection framework in resource constrained internet of things domain. In: 2019 IEEE 8th Glob. Conf. Consum. Electron. GCCE 2019, pp. 675\u2013678 (2019).https:\/\/doi.org\/10.1109\/GCCE46687.2019.9015465","DOI":"10.1109\/GCCE46687.2019.9015465"},{"key":"803_CR45","doi-asserted-by":"publisher","DOI":"10.3390\/electronics8111210","author":"A Khraisat","year":"2019","unstructured":"Khraisat, A., Gondal, I., Vamplew, P., Kamruzzaman, J., Alazab, A.: A novel ensemble of hybrid intrusion detection system for detecting internet of things attacks. Electron (2019). https:\/\/doi.org\/10.3390\/electronics8111210","journal-title":"Electron"},{"key":"803_CR46","doi-asserted-by":"publisher","unstructured":"Alazab, A., Abawajy, J., Hobbs, M., Layton, R.: Crime Toolkits\u202f: The Productisation of Cybercrime (2013). https:\/\/doi.org\/10.1109\/TrustCom.2013.273","DOI":"10.1109\/TrustCom.2013.273"},{"key":"803_CR47","doi-asserted-by":"publisher","unstructured":"Singh, J., Pasquier, T., Bacon, J., Ko, H., Eyers, D.: Twenty security considerations for cloud-supported Internet of Things. vol. 4662, no. c, pp. 1\u201316 (2015). https:\/\/doi.org\/10.1109\/JIOT.2015.2460333","DOI":"10.1109\/JIOT.2015.2460333"},{"key":"803_CR48","doi-asserted-by":"publisher","unstructured":"Adeyiola, A.Q., Saheed, Y.K., Misra, S., Chockalingam, S.: Metaheuristic firefly and C5 . 0 algorithms based intrusion detection for critical infrastructures. In: 2023 3rd International Conference on Applied Artificial Intelligence (ICAPAI), pp. 1\u20137 (2023). https:\/\/doi.org\/10.1109\/ICAPAI58366.2023.10193917","DOI":"10.1109\/ICAPAI58366.2023.10193917"},{"issue":"7","key":"803_CR49","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1109\/MC.2017.201","volume":"50","author":"C Kolias","year":"2017","unstructured":"Kolias, C., Kambourakis, G., Stavrou, A., Voas, J.: DDoS in the IoT: Mirai and other botnets. Computer (Long Beach Calif.) 50(7), 80\u201384 (2017). https:\/\/doi.org\/10.1109\/MC.2017.201","journal-title":"Computer (Long Beach Calif.)"},{"key":"803_CR50","doi-asserted-by":"publisher","unstructured":"Abomhara, M., K\u00f8ien, G.M.: Cyber security and the internet of things\u202f: vulnerabilities , threats , intruders.4, 65\u201388 (2015). https:\/\/doi.org\/10.13052\/jcsm2245-1439.414","DOI":"10.13052\/jcsm2245-1439.414"},{"key":"803_CR51","doi-asserted-by":"publisher","first-page":"779","DOI":"10.1016\/j.future.2019.05.041","volume":"100","author":"N Koroniotis","year":"2019","unstructured":"Koroniotis, N., Moustafa, N., Sitnikova, E., Turnbull, B.: Towards the development of realistic botnet dataset in the Internet of Things for network forensic analytics: Bot-IoT dataset. Futur. Gener. Comput. Syst. 100, 779\u2013796 (2019). https:\/\/doi.org\/10.1016\/j.future.2019.05.041","journal-title":"Futur. Gener. Comput. Syst."},{"issue":"11","key":"803_CR52","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1016\/S1353-4858(16)30104-0","volume":"2016","author":"S Mansfield-devine","year":"2016","unstructured":"Mansfield-devine, S., Security, N.: DDoS goes mainstream: attacks could make this threat an organisation \u2019 s biggest nightmare. Netw. Secur. 2016(11), 7\u201313 (2016). https:\/\/doi.org\/10.1016\/S1353-4858(16)30104-0","journal-title":"Netw. Secur."},{"key":"803_CR53","unstructured":"Greenberg, A.: Hackers remotely kill a jeep on the highway\u2014with me in it. Wired, 7(21) (2015)"},{"key":"803_CR54","doi-asserted-by":"crossref","unstructured":"Saheed, Y.K.: Data analytics for intrusion detection system based on recurrent neural network and supervised machine learning methods. In: Recurrent Neural Networks, pp. 167\u2013179. CRC Press Taylor & Francis Group (2022)","DOI":"10.1201\/9781003307822-12"},{"key":"803_CR55","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1016\/j.eswa.2018.04.008","volume":"106","author":"S Jain","year":"2018","unstructured":"Jain, S., Shukla, S., Wadhvani, R.: Dynamic selection of normalization techniques using data complexity measures. Expert Syst. Appl. 106, 252\u2013262 (2018). https:\/\/doi.org\/10.1016\/j.eswa.2018.04.008","journal-title":"Expert Syst. Appl."},{"key":"803_CR56","doi-asserted-by":"publisher","DOI":"10.3390\/rs10020222","author":"S Georganos","year":"2018","unstructured":"Georganos, S., Lennert, M., Grippa, T., Vanhuysse, S., Johnson, B., Wolff, E.: Normalization in unsupervised segmentation parameter optimization: a solution based on local regression trend analysis. Remote Sens. (2018). https:\/\/doi.org\/10.3390\/rs10020222","journal-title":"Remote Sens."},{"key":"803_CR57","volume-title":"Artificial Intelligence for Cloud and Edge Computing. Internet of Things (Technology, Communications and Computing)","author":"YK Saheed","year":"2022","unstructured":"Saheed, Y.K.: Performance improvement of intrusion detection system for detecting attacks on internet of things and edge of things. In: Misra, S., Kumar, T.A., Piuri, V., Garg, L. (eds.) Artificial Intelligence for Cloud and Edge Computing. Internet of Things (Technology, Communications and Computing). Springer, Cham (2022)"},{"key":"803_CR58","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4419-7970-4","volume-title":"Entropy and Information Theory","author":"RM Gray","year":"2011","unstructured":"Gray, R.M.: Entropy and Information Theory. Springer Science & Business Media (2011)"},{"key":"803_CR59","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jnca.2017.04.015","volume":"91","author":"E Adi","year":"2017","unstructured":"Adi, E., Baig, Z., Hingston, P.: Stealthy Denial of Service (DoS) attack modelling and detection for HTTP\/2 services. J. Netw. Comput. Appl. 91, 1\u201313 (2017). https:\/\/doi.org\/10.1016\/j.jnca.2017.04.015","journal-title":"J. Netw. Comput. Appl."},{"key":"803_CR60","doi-asserted-by":"crossref","unstructured":"Saheed, Y.K.: Machine learning-based blockchain technology for protection and privacy against intrusion attacks in intelligent transportation systems. In: Machine Learning, Blockchain Technologies and Big Data Analytics for IoTs: Methods, Technologies and Applications, p. 16 (2022)","DOI":"10.1049\/PBSE016E_ch15"},{"key":"803_CR61","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/j.eswa.2016.06.004","volume":"62","author":"E ZorarpacI","year":"2016","unstructured":"ZorarpacI, E., \u00d6zel, S.A.: A hybrid approach of differential evolution and artificial bee colony for feature selection. Expert Syst. Appl. 62, 91\u2013103 (2016). https:\/\/doi.org\/10.1016\/j.eswa.2016.06.004","journal-title":"Expert Syst. Appl."},{"key":"803_CR62","unstructured":"Jimoh, R.G., Ridwan, M.Y., Yusuf, O.O., Saheed, Y.K.: Application of dimensionality reduction on classification of colon cancer using Ica and K-Nn algorithm. Anale. Ser. Informatic\u0103, vol. 6, no. 10, pp. 55\u201359, 2018, [Online]. Available: http:\/\/anale-informatica.tibiscus.ro\/download\/lucrari\/16-1-06-Olatunde.pdf."},{"key":"803_CR63","doi-asserted-by":"crossref","unstructured":"Seni, G., Elder, J.F.: Ensemble Methods in Data Mining: Improving Accuracy Through Combining Predictions, vol. 2, no. 1 (2010)","DOI":"10.2200\/S00240ED1V01Y200912DMK002"},{"issue":"3 PART 1","key":"803_CR64","doi-asserted-by":"publisher","first-page":"5297","DOI":"10.1016\/j.eswa.2008.06.068","volume":"36","author":"C Hung","year":"2009","unstructured":"Hung, C., Chen, J.H.: A selective ensemble based on expected probabilities for bankruptcy prediction. Expert Syst. Appl. 36(3 PART 1), 5297\u20135303 (2009). https:\/\/doi.org\/10.1016\/j.eswa.2008.06.068","journal-title":"Expert Syst. Appl."},{"key":"803_CR65","doi-asserted-by":"publisher","DOI":"10.3390\/electronics8030322","author":"R Abdulhammed","year":"2019","unstructured":"Abdulhammed, R., Musafer, H., Alessa, A., Faezipour, M., Abuzneid, A.: Features dimensionality reduction approaches for machine learning based network intrusion detection. Electron (2019). https:\/\/doi.org\/10.3390\/electronics8030322","journal-title":"Electron"},{"issue":"1","key":"803_CR66","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1016\/j.eswa.2014.08.002","volume":"42","author":"S Elhag","year":"2015","unstructured":"Elhag, S., Fern\u00e1ndez, A., Bawakid, A., Alshomrani, S., Herrera, F.: On the combination of genetic fuzzy systems and pairwise learning for improving detection rates on Intrusion Detection Systems. Expert Syst. Appl. 42(1), 193\u2013202 (2015). https:\/\/doi.org\/10.1016\/j.eswa.2014.08.002","journal-title":"Expert Syst. Appl."},{"issue":"4","key":"803_CR67","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1145\/382912.382923","volume":"3","author":"J Mchugh","year":"2000","unstructured":"Mchugh, J.: Testing intrusion detection systems: a critique of the 1998 and 1999 DARPA intrusion detection system evaluations as performed by Lincoln Laboratory. ACM Trans. Inf. Syst. Secur. 3(4), 262\u2013294 (2000). https:\/\/doi.org\/10.1145\/382912.382923","journal-title":"ACM Trans. Inf. Syst. Secur."},{"key":"803_CR68","doi-asserted-by":"crossref","unstructured":"Tavallaee, M., Bagheri, E., Lu, W., Ghorbani, A.A.: A detailed analysis of the KDD CUP 99 data set in computational intelligence for security and defense applications. Comput. Intell. Secur. Def. Appl., no. Cisda, 1\u20136 (2009)","DOI":"10.1109\/CISDA.2009.5356528"},{"key":"803_CR69","unstructured":"Sangster, B. et al.: Toward instrumenting network warfare competitions to generate labeled datasets. In: 2nd Work. Cyber Secur. Exp. Test, CSET 2009 (2009)"},{"key":"803_CR70","doi-asserted-by":"publisher","unstructured":"Sato, M., Yamaki, H., Takakura, H.: Unknown attacks detection using feature extraction from anomaly-based IDS alerts. In: Proc.\u20142012 IEEE\/IPSJ 12th Int. Symp. Appl. Internet, SAINT 2012, pp. 273\u2013277 (2012). https:\/\/doi.org\/10.1109\/SAINT.2012.51","DOI":"10.1109\/SAINT.2012.51"},{"key":"803_CR71","doi-asserted-by":"publisher","unstructured":"Sperotto, A., Sadre, R., Van Vliet, F., Pras, A.: A labeled data set for flow-based intrusion detection. In: IP Operations and Management: 9th IEEE International Workshop, IPOM, pp. 39\u201350 (2009). https:\/\/doi.org\/10.1007\/978-3-642-04968-2_4","DOI":"10.1007\/978-3-642-04968-2_4"},{"issue":"3","key":"803_CR72","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1016\/j.cose.2011.12.012","volume":"31","author":"A Shiravi","year":"2012","unstructured":"Shiravi, A., Shiravi, H., Tavallaee, M., Ghorbani, A.A.: Toward developing a systematic approach to generate benchmark datasets for intrusion detection. Comput. Secur. 31(3), 357\u2013374 (2012). https:\/\/doi.org\/10.1016\/j.cose.2011.12.012","journal-title":"Comput. Secur."},{"key":"803_CR73","doi-asserted-by":"publisher","unstructured":"Lippmann, R.P. et al.: Evaluating intrusion detection systems: the 1998 DARPA off-line intrusion detection evaluation. In: Proc. - DARPA Inf. Surviv. Conf. Expo. DISCEX 2000, vol. 2, pp. 12\u201326 (2000). https:\/\/doi.org\/10.1109\/DISCEX.2000.821506","DOI":"10.1109\/DISCEX.2000.821506"},{"key":"803_CR74","unstructured":"Ruoming, P., Mark, A., Mike, B., Jason, L., Vern, P., Brian, T.: A first look at modern enterprise traffic. In: p. Proceedings of the 5th ACM SIGCOMM conference on I (2005)"},{"key":"803_CR75","doi-asserted-by":"publisher","DOI":"10.1109\/AHICI.2011.6113948","author":"AR Vasudevan","year":"2011","unstructured":"Vasudevan, A.R., Harshini, E., Selvakumar, S.: SSENet-2011: a network intrusion detection system dataset and its comparison with KDD CUP 99 dataset. Asian Himalayas Int. Conf. Internet (2011). https:\/\/doi.org\/10.1109\/AHICI.2011.6113948","journal-title":"Asian Himalayas Int. Conf. Internet"},{"issue":"5","key":"803_CR76","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1145\/1629607.1629610","volume":"39","author":"F Gringoli","year":"2009","unstructured":"Gringoli, F., Salgarelli, L., Cascarano, N., Risso, F., Claffy, K.C., Rodriguez, P.: GT: picking up the truth from the ground in traffic classification. ACM SIGCOMM Comput. Commun. Rev. 39(5), 12\u201318 (2009)","journal-title":"ACM SIGCOMM Comput. Commun. Rev."},{"key":"803_CR77","doi-asserted-by":"publisher","unstructured":"Beigi, E.B., Jazi, H.H., Stakhanova, N., Ghorbani, A.A.: Towards effective feature selection in machine learning-based botnet detection approaches. In: 2014 IEEE Conf. Commun. Netw. Secur. CNS 2014, pp. 247\u2013255 (2014).https:\/\/doi.org\/10.1109\/CNS.2014.6997492","DOI":"10.1109\/CNS.2014.6997492"},{"issue":"1","key":"803_CR78","doi-asserted-by":"publisher","first-page":"436","DOI":"10.14569\/ijacsa.2016.070159","volume":"7","author":"M Alkasassbeh","year":"2016","unstructured":"Alkasassbeh, M., Al-Naymat, G., B.A, A., Almseidin, M.: Detecting distributed denial of service attacks using data mining techniques. Int. J. Adv. Comput. Sci. Appl. 7(1), 436\u2013445 (2016). https:\/\/doi.org\/10.14569\/ijacsa.2016.070159","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"issue":"1","key":"803_CR79","doi-asserted-by":"publisher","first-page":"177","DOI":"10.13052\/jsn2445-9739.2017.009","volume":"2017","author":"I Sharafaldin","year":"2017","unstructured":"Sharafaldin, I., Gharib, A., Lashkari, A.H., Ghorbani, A.A.: Towards a reliable intrusion detection benchmark dataset. Softw. Netw. 2017(1), 177\u2013200 (2017). https:\/\/doi.org\/10.13052\/jsn2445-9739.2017.009","journal-title":"Softw. Netw."},{"issue":"3","key":"803_CR80","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1109\/MPRV.2018.03367731","volume":"17","author":"Y Meidan","year":"2018","unstructured":"Meidan, Y., et al.: N-BaIoT-Network-based detection of IoT botnet attacks using deep autoencoders. IEEE Pervasive Comput. 17(3), 12\u201322 (2018). https:\/\/doi.org\/10.1109\/MPRV.2018.03367731","journal-title":"IEEE Pervasive Comput."},{"key":"803_CR81","doi-asserted-by":"publisher","unstructured":"Ahmed, S.W., Kientz, F., Kashef, R.: A modified transformer neural network (MTNN) for robust intrusion detection in IoT networks. In: 2023 Int. Telecommun. Conf. ITC-Egypt 2023, pp. 663\u2013668 (2023).https:\/\/doi.org\/10.1109\/ITC-Egypt58155.2023.10206134","DOI":"10.1109\/ITC-Egypt58155.2023.10206134"},{"issue":"December 2022","key":"803_CR82","doi-asserted-by":"publisher","first-page":"103402","DOI":"10.1016\/j.advengsoft.2022.103402","volume":"176","author":"M Abd Elaziz","year":"2023","unstructured":"Abd Elaziz, M., Al-qaness, M.A.A., Dahou, A., Ibrahim, R.A., El-Latif, A.A.A.: Intrusion detection approach for cloud and IoT environments using deep learning and Capuchin Search Algorithm. Adv. Eng. Softw. 176(December 2022), 103402 (2023). https:\/\/doi.org\/10.1016\/j.advengsoft.2022.103402","journal-title":"Adv. Eng. Softw."},{"issue":"9","key":"803_CR83","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/s23094430","volume":"23","author":"A Fatani","year":"2023","unstructured":"Fatani, A., et al.: Enhancing intrusion detection systems for IoT and cloud environments using a growth optimizer algorithm and conventional neural networks. Sensors 23(9), 1\u201314 (2023). https:\/\/doi.org\/10.3390\/s23094430","journal-title":"Sensors"}],"container-title":["International Journal of Information Security"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10207-023-00803-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10207-023-00803-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10207-023-00803-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,27]],"date-time":"2024-05-27T02:02:25Z","timestamp":1716775345000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10207-023-00803-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,9]]},"references-count":83,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,6]]}},"alternative-id":["803"],"URL":"https:\/\/doi.org\/10.1007\/s10207-023-00803-x","relation":{},"ISSN":["1615-5262","1615-5270"],"issn-type":[{"value":"1615-5262","type":"print"},{"value":"1615-5270","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,9]]},"assertion":[{"value":"11 December 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 January 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Authors do not have any financial or non-financial interests that are directly or indirectly related to the work submitted for publication.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Authors comply with the highest level of ethical standards while preparing the manuscript.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}