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Ind. Inform."},{"key":"ref4","doi-asserted-by":"crossref","first-page":"4535","DOI":"10.1109\/JSYST.2019.2963235","article-title":"A lightweight mutual authentication for smart grid neighborhood area network communications based on physically unclonable function","volume":"14","author":"Kaveh","year":"2020","journal-title":"IEEE Syst. J."},{"key":"ref5","article-title":"An efficient authentication protocol for smart grid communication based on on-chip-error-correcting physical unclonable function","volume":"36","author":"Kaveh","year":"2023, Art. no. 101228","journal-title":"Sustain. Energy Grid. Netw."},{"key":"ref6","doi-asserted-by":"crossref","first-page":"5258","DOI":"10.1109\/TIFS.2023.3305916","article-title":"A smart digital twin enabled security framework for vehicle-to-grid cyber-physical systems","volume":"18","author":"Ali","year":"2023","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref7","doi-asserted-by":"crossref","DOI":"10.3390\/electronics9091479","article-title":"A lightweight authentication scheme for V2G communications: A PUF-based approach ensuring cyber\/physical security and identity\/location privacy","volume":"9","author":"Kaveh","year":"2020, Art. no. 1479","journal-title":"Electronics"},{"key":"ref8","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1109\/TIV.2023.3337898","article-title":"Performance analysis of RIS\/STAR-IOS-aided V2V NOMA\/OMA communications over composite fading channels","volume":"9","author":"Ghadi","year":"2023","journal-title":"IEEE Trans. Intell. Vehicles"},{"key":"ref9","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.124317","article-title":"Analysis of extreme learning machines (ELMs) for intelligent intrusion detection systems: A survey","volume":"253","author":"Abu Al-Haija","year":"2024, Art. no. 124317","journal-title":"Expert. Syst. Appl."},{"key":"ref10","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.inffus.2022.10.008","article-title":"Deep learning for anomaly detection in multivariate time series: Approaches, applications, and challenges","volume":"91","author":"Li","year":"2023","journal-title":"Inf. Fusion"},{"key":"ref11","doi-asserted-by":"crossref","DOI":"10.1016\/j.iot.2024.101162","article-title":"A comparative analysis of various machine learning methods for anomaly detection in cyber attacks on IoT networks","volume":"26","author":"Inuwa","year":"2024, Art. no. 101162","journal-title":"Internet Things"},{"key":"ref12","doi-asserted-by":"crossref","first-page":"3392","DOI":"10.1007\/s11227-022-04783-y","article-title":"An improved anomaly detection model for IoT security using decision tree and gradient boosting","volume":"79","author":"Douiba","year":"2023","journal-title":"J. Supercomput."},{"key":"ref13","doi-asserted-by":"crossref","DOI":"10.1016\/j.epsr.2022.108975","article-title":"A comprehensive review of cyber-attacks and defense mechanisms for improving security in smart grid energy systems: Past, present and future","volume":"215","author":"Ghiasi","year":"2023, Art. no. 108975","journal-title":"Electr. Power Syst. Res."},{"key":"ref14","doi-asserted-by":"crossref","first-page":"708","DOI":"10.1016\/j.procs.2015.08.220","article-title":"Survey on anomaly detection using data mining techniques","volume":"60","author":"Agrawal","year":"2015","journal-title":"Procedia Comput. Sci."},{"key":"ref15","doi-asserted-by":"crossref","DOI":"10.1016\/j.cose.2024.103733","article-title":"Future of generative adversarial networks (GAN) for anomaly detection in network security: A review","volume":"139","author":"Lim","year":"2024","journal-title":"Comput. Secur."},{"key":"ref16","unstructured":"S. Hettich and S. D. Bay, \u201cKDD cup 1999 data,\u201d 1999. Accessed: Dec. 04, 2024. [Online]. Available: http:\/\/kdd.ics.uci.edu\/databases\/kddcup99\/kddcup99.html"},{"key":"ref17","doi-asserted-by":"crossref","first-page":"580","DOI":"10.5937\/vojtehg66-16670","article-title":"Review of KDD Cup\u201999, NSL-KDD and Kyoto 2006+ datasets","volume":"66","author":"Proti\u0107","year":"2018","journal-title":"Vojnotehni\u010dki Glasnik\/Military Tech. Courier"},{"key":"ref18","doi-asserted-by":"crossref","DOI":"10.1186\/s40537-020-00379-6","article-title":"Performance analysis of intrusion detection systems using a feature selection method on the UNSW-NB15 dataset","volume":"7","author":"Kasongo","year":"2020","journal-title":"J. Big Data"},{"key":"ref19","unstructured":"A. Parmisano, S. Garcia, and M. J. Erquiaga, \u201cA labeled dataset with malicious and benign IoT network traffic,\u201d 2020. Accessed: Dec. 04, 2024. [Online]. Available: https:\/\/www.stratosphereips.org\/datasets-iot23"},{"key":"ref20","first-page":"2043","article-title":"Anomaly detection using ensemble random forest in wireless sensor network","volume":"13","author":"Biswas","year":"2021","journal-title":"Int. J. Inform. Technol."},{"key":"ref21","doi-asserted-by":"crossref","DOI":"10.3390\/sym15061205","article-title":"Real-time anomaly detection of network traffic based on CNN","volume":"15","author":"Liu","year":"2023, Art. no. 1205","journal-title":"Symmetry"},{"key":"ref22","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2021.115524","article-title":"A bidirectional LSTM deep learning approach for intrusion detection","volume":"185","author":"Imrana","year":"2021, Art. no. 115524","journal-title":"Expert. Syst. Appl."},{"key":"ref23","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2020.114170","article-title":"Deep belief network-based intrusion detection techniques: A survey","volume":"167","author":"Sohn","year":"2021, Art. no. 114170","journal-title":"Expert. Syst. Appl."},{"key":"ref24","series-title":"Proc. Int. Conf. Signal Process. Commun. Eng. Syst.","first-page":"92","article-title":"Performance analysis of NSL-KDD dataset using ANN","author":"Ingre","year":"Jan. 2015"},{"key":"ref25","doi-asserted-by":"crossref","first-page":"50927","DOI":"10.1109\/ACCESS.2018.2868171","article-title":"A novel semi-supervised learning approach for network intrusion detection on cloud-based robotic system","volume":"6","author":"Gao","year":"2018","journal-title":"IEEE Access"},{"key":"ref26","series-title":"Proc. IEEE 15th Int. Conf. Mach. Learn. Appl. (ICMLA)","first-page":"195","article-title":"Toward an online anomaly intrusion detection system based on deep learning","author":"Alrawashdeh","year":"2016"},{"key":"ref27","series-title":"Proc. Int. Conf. Wirel. Netw. Mobile Commun. (WINCOM)","first-page":"258","article-title":"Deep learning approach for network intrusion detection in software defined networking","author":"Tang","year":"2016"},{"key":"ref28","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1089\/big.2018.0023","article-title":"Deep learning method for denial-of-service attack detection based on restricted Boltzmann machine","volume":"6","author":"Imamverdiyev","year":"2018","journal-title":"Big Data"},{"key":"ref29","doi-asserted-by":"crossref","first-page":"181","DOI":"10.26599\/BDMA.2020.9020003","article-title":"Applying big data based deep learning system to intrusion detectio","volume":"3","author":"Zhong","year":"2020","journal-title":"Big Data Min. Anal."},{"key":"ref30","doi-asserted-by":"crossref","first-page":"484","DOI":"10.26599\/TST.2020.9010022","article-title":"Intrusion detection system using voting based neural network","volume":"26","author":"Haghighat","year":"2021","journal-title":"Tsinghua Sci. Technol."},{"key":"ref31","doi-asserted-by":"crossref","first-page":"3007","DOI":"10.1109\/TNSE.2022.3157730","article-title":"ASTREAM: Data-stream-driven scalable anomaly detection with accuracy guarantee in IIoT environment","volume":"10","author":"Yang","year":"Mar. 2022","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"key":"ref32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2133360.2133363","article-title":"Isolation-based anomaly detection","volume":"6","author":"Liu","year":"2012","journal-title":"ACM Trans. Knowl. Discov. Data."},{"key":"ref33","series-title":"Proc. IEEE 33rd Int. Conf. Data Eng. (ICDE)","first-page":"983","article-title":"LSHiForest: A generic framework for fast tree isolation-based ensemble anomaly analysis","author":"Zhang","year":"Apr. 2017"},{"key":"ref34","doi-asserted-by":"crossref","first-page":"6503","DOI":"10.1109\/TII.2021.3139363","article-title":"Fast anomaly identification based on multi-aspect data streams for intelligent intrusion detection toward secure Industry 4. 0","volume":"18","author":"Qi","year":"2022","journal-title":"IEEE Trans. Ind. Informat."},{"key":"ref35","series-title":"Proc. Int. Conf. Platform Technol. Serv. (PlatCon)","first-page":"1","article-title":"Long short-term memory recurrent neural network classifier for intrusion detection","author":"Kim","year":"2016"},{"key":"ref36","doi-asserted-by":"crossref","first-page":"21954","DOI":"10.1109\/ACCESS.2017.2762418","article-title":"A deep learning approach for intrusion detection using recurrent neural networks","volume":"5","author":"Yin","year":"2017","journal-title":"IEEE Access"},{"key":"ref37","doi-asserted-by":"crossref","first-page":"48697","DOI":"10.1109\/ACCESS.2018.2867564","article-title":"An intrusion detection system using a deep neural network with gated recurrent units","volume":"6","author":"Xu","year":"2018","journal-title":"IEEE Access"},{"key":"ref38","doi-asserted-by":"crossref","first-page":"951","DOI":"10.1109\/JIOT.2020.3009180","article-title":"Omni SCADA intrusion detection using deep learning algorithms","volume":"8","author":"Gao","year":"2021","journal-title":"IEEE Internet Things"},{"key":"ref39","article-title":"A deep learning approach for network intrusion detection system, EAI Endorsed Trans","volume":"16","author":"Javaid","year":"2016","journal-title":"Secur. Saf."},{"key":"ref40","doi-asserted-by":"crossref","first-page":"41238","DOI":"10.1109\/ACCESS.2018.2858277","article-title":"Effective feature extraction via stacked sparse autoencoder to improve intrusion detection system","volume":"6","author":"Yan","year":"2018","journal-title":"IEEE Access"},{"key":"ref41","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1109\/TETCI.2017.2772792","article-title":"A deep learning approach to network intrusion detection","volume":"2","author":"Shone","year":"2018","journal-title":"IEEE Trans. Emerg. Topics Comput. Intell."},{"key":"ref42","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.neucom.2019.11.016","article-title":"A novel statistical analysis and autoencoder driven intelligent intrusion detection approach","volume":"387","author":"Ieracitano","year":"2020","journal-title":"Neurocomputing"},{"key":"ref43","series-title":"Proc. Int. Conf. NeuralInf Process.","first-page":"556","article-title":"Malware detection using deep transferred generative adversarial networks","author":"Kim","year":"2017"},{"key":"ref44","series-title":"Proc. IEEE 44th Annu. Comput. Softw. Appl. Conf. (COMPSAC)","first-page":"376","article-title":"G-IDS: Generative adversarial networks assisted intrusion detection system","author":"Shahriar","year":"Jul. 2020"},{"key":"ref45","series-title":"Proc. IEEE 21st Int. Conf. Inf. Reuse Integr. Data Sci. (IRI)","first-page":"25","article-title":"Addressing imbalanced data problem with generative adversarial network for intrusion detection","author":"Yilmaz","year":"2020"},{"key":"ref46","series-title":"Proc. IEEE 44th Annu. Comput. Softw. Appl. Conf. (COMPSAC)","first-page":"817","article-title":"Improving attack detection performance in NIDS using GAN","author":"Li","year":"Jul. 2020"},{"key":"ref47","series-title":"Proc. Int. Conf. Internet Distrib. Comput. Syst.","first-page":"306","article-title":"Generation of network traffic using WGAN-GP and a DFT filter for resolving data imbalance","author":"Lee","year":"Oct. 2019"},{"key":"ref48","doi-asserted-by":"crossref","first-page":"13635","DOI":"10.1007\/s00521-021-05993-w","article-title":"DGM: A data generative model to improve minority class presence in anomaly detection domain","volume":"33","author":"Dlamini","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"ref49","unstructured":"D. Li, D. Chen, J. Goh, and S. K. Ng, \u201cAnomaly detection with generative adversarial networks for multivariate time series,\u201d 2018, arXiv:1809.04758."},{"key":"ref50","series-title":"Proc. Global Smart Ind. Conf. (GloSIC)","first-page":"199","article-title":"Applying of generative adversarial networks for anomaly detection in industrial control systems","author":"Alabugin","year":"Nov. 2020"},{"key":"ref51","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TNSM.2021.3078381","article-title":"A unified deep learning anomaly detection and classification approach for smart grid environments","volume":"18","author":"Siniosoglou","year":"2021","journal-title":"IEEE Trans. Netw. Service Manage"},{"key":"ref52","doi-asserted-by":"crossref","first-page":"2330","DOI":"10.1109\/JIOT.2022.3211346","article-title":"An enhanced AI-based network intrusion detection system using generative adversarial networks","volume":"10","author":"Park","year":"2022","journal-title":"IEEE Internet Things"},{"key":"ref53","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1007\/s10586-022-03621-3","article-title":"DMAIDPS: A distributed multi-agent intrusion detection and prevention system for cloud IoT environments","volume":"26","author":"Javadpour","year":"2023","journal-title":"Cluster Comput."},{"key":"ref54","doi-asserted-by":"crossref","first-page":"599","DOI":"10.1007\/s10586-022-03629-9","article-title":"A hybrid heuristics artificial intelligence feature selection for intrusion detection classifiers in cloud of things","volume":"26","author":"Sangaiah","year":"2023","journal-title":"Cluster Comput."},{"key":"ref55","doi-asserted-by":"crossref","DOI":"10.1007\/s43926-024-00060-x","article-title":"Maximizing intrusion detection efficiency for IoT networks using extreme learning machine","volume":"4","author":"Altamimi","year":"2024","journal-title":"Discover Internet Things"},{"key":"ref56","series-title":"Proc. 34th Int. Conf. Mach. Learn. (ICML)","first-page":"214","article-title":"Wasserstein generative adversarial networks","author":"Arjovsky","year":"2017"},{"key":"ref57","unstructured":"D. Berthelot, T. Schumm, and L. 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