{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T19:00:00Z","timestamp":1771614000431,"version":"3.50.1"},"reference-count":62,"publisher":"Springer Science and Business Media LLC","issue":"25","license":[{"start":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T00:00:00Z","timestamp":1753833600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T00:00:00Z","timestamp":1753833600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2025,9]]},"DOI":"10.1007\/s00521-025-11452-7","type":"journal-article","created":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T07:42:20Z","timestamp":1753861340000},"page":"21311-21346","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Fortifying EV charging stations: AI-powered detection and mitigation of DDoS attacks using personalized Federated learning"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6116-2191","authenticated-orcid":false,"given":"Fatma M.","family":"Talaat","sequence":"first","affiliation":[]},{"given":"Mohamed Mohsen Elsaid","family":"Khoudier","sequence":"additional","affiliation":[]},{"given":"Ibrahim F.","family":"Moawad","sequence":"additional","affiliation":[]},{"given":"Amir","family":"El-Ghamry","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,7,30]]},"reference":[{"key":"11452_CR1","unstructured":"The UK Govt Strategy. \u201cRoad to Zero\u201d by 2050. Available online: https:\/\/assets.publishing.service.gov.uk\/government\/uploads\/system\/uploads\/attachment_data\/file\/739460\/road-to-zero.pdf. Accessed 21 Jan 2023"},{"key":"11452_CR2","unstructured":"IEA. Global EV Outlook 2020. Available online: https:\/\/www.iea.org\/reports\/global-ev-outlook-2020. Accessed 22 Jan 2023"},{"key":"11452_CR3","unstructured":"Kaspersky. Remotely Controlled EV Home Chargers\u2014The Threats and Vulnerabilities. 2018. Available online: https:\/\/securelist.com\/remotely-controlled-ev-home-chargers-the-threats-and-vulnerabilities\/89251\/. Accessed 11 Mar 2023"},{"issue":"1","key":"11452_CR4","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1080\/23742917.2022.2135856","volume":"7","author":"A Suhag","year":"2023","unstructured":"Suhag A, Daniel DA (2023) Study of statistical techniques and artificial intelligence methods in distributed denial of service (DDOS) assault and defense. J Cyber Secur Technol 7(1):21\u201351. https:\/\/doi.org\/10.1080\/23742917.2022.2135856","journal-title":"J Cyber Secur Technol"},{"key":"11452_CR5","doi-asserted-by":"publisher","first-page":"76","DOI":"10.3390\/fi15020076","volume":"15","author":"S Ahmed","year":"2023","unstructured":"Ahmed S, Khan ZA, Mohsin SM, Latif S, Aslam S, Mujlid H, Adil M, Najam Z (2023) Effective and efficient DDoS attack detection using deep learning algorithm Multi-Layer Perceptron. Future Int 15:76. https:\/\/doi.org\/10.3390\/fi15020076","journal-title":"Future Int"},{"key":"11452_CR6","doi-asserted-by":"publisher","DOI":"10.1002\/eng2.12697","author":"M Najafimehr","year":"2023","unstructured":"Najafimehr M, Mostafav SZS (2023) DDoS attacks and machine-learning-based detectionmethods: a survey and taxonomy. Eng Rep. https:\/\/doi.org\/10.1002\/eng2.12697","journal-title":"Eng Rep"},{"issue":"99","key":"11452_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/COMST.2019.2934468","volume":"PP","author":"N Agrawal","year":"2019","unstructured":"Agrawal N, Tapaswi S (2019) Defense mechanisms against DDoS attacks in a cloud computing environment: state-of-the-art and research challenges. IEEE Commun Surv Tutor PP(99):1\u20131. https:\/\/doi.org\/10.1109\/COMST.2019.2934468","journal-title":"IEEE Commun Surv Tutor"},{"key":"11452_CR8","doi-asserted-by":"publisher","first-page":"3333","DOI":"10.3390\/s23063333","volume":"23","author":"R Abu Bakar","year":"2023","unstructured":"Abu Bakar R, Huang X, Javed MS, Hussain S, Majeed MF (2023) An intelligent agent-based detection system for DDoS attacks using automatic feature extraction and selection. Sensors 23:3333. https:\/\/doi.org\/10.3390\/s23063333","journal-title":"Sensors"},{"key":"11452_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.iot.2023.100825","author":"I Ahmad","year":"2023","unstructured":"Ahmad I, Wan Z, Ahmad A (2023) A big data analytics for DDOS attack detection using optimized ensemble framework in Internet of Things. Int Thing. https:\/\/doi.org\/10.1016\/j.iot.2023.100825","journal-title":"Int Thing"},{"key":"11452_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.106432","author":"J Bhayo","year":"2023","unstructured":"Bhayo J, Shah SA, Hameed S, Ahmed A, Nasir J, Draheim D (2023) Towards a machine learning-based framework for DDOS attack detection in software-defined IoT (SD-IoT) networks. Eng Appl Artific Intell. https:\/\/doi.org\/10.1016\/j.engappai.2023.106432","journal-title":"Eng Appl Artific Intell"},{"issue":"2","key":"11452_CR11","doi-asserted-by":"publisher","first-page":"76","DOI":"10.3390\/fi15020076","volume":"15","author":"S Ahmed","year":"2023","unstructured":"Ahmed S, Khan ZA, Mohsin SM, Latif S, Aslam S, Mujlid H, Adil M, Najam Z (2023) Effective and efficient DDoS attack detection using deep learning algorithm, multi-layer perceptron. Future Internet 15(2):76. https:\/\/doi.org\/10.3390\/fi15020076","journal-title":"Future Internet"},{"key":"11452_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.122874","author":"F Sabah","year":"2024","unstructured":"Sabah F, Chen Y, Yang Z, Azam M, Ahmad N, Sarwar R (2024) Model optimization techniques in personalized federated learning: a survey. Expert Syst Appl. https:\/\/doi.org\/10.1016\/j.eswa.2023.122874","journal-title":"Expert Syst Appl"},{"issue":"16","key":"11452_CR13","doi-asserted-by":"publisher","first-page":"1910","DOI":"10.3390\/electronics10161910","volume":"10","author":"SM Arif","year":"2021","unstructured":"Arif SM, Lie TT, Seet BC, Ayyadi S, Jensen K (2021) Review of electric vehicle technologies, charging methods, standards and optimization techniques. Electronics 10(16):1910","journal-title":"Electronics"},{"key":"11452_CR14","unstructured":"Russian EV Chargers Hacked, Screen Reads \u201cGlory To Ukraine!\u201d (n.d.). InsideEVs. Retrieved July 2, 2023, from https:\/\/insideevs.com\/news\/570958\/russia-electric-car-chargershacked\/"},{"key":"11452_CR15","doi-asserted-by":"crossref","unstructured":"K\u00c5Nohler S, Baker R, Strohmeier M, Martinovic I (2022) Brokenwire: Wireless disruption of ccs electric vehicle charging. arXiv preprint arXiv:2202.02104.","DOI":"10.14722\/ndss.2023.23251"},{"key":"11452_CR16","unstructured":"McMahan B, Moore E, Ramage D, Hampson S, Arcas YBA (2017) Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics (pp. 1273\u20131282). PMLR."},{"issue":"16","key":"11452_CR17","doi-asserted-by":"publisher","first-page":"2627","DOI":"10.3390\/electronics11162627","volume":"11","author":"X Sun","year":"2022","unstructured":"Sun X, Tang Z, Du M, Deng C, Lin W, Chen J, Zheng H (2022) A hierarchical federated learning-based intrusion detection system for 5G smart grids. Electronics 11(16):2627. https:\/\/doi.org\/10.3390\/electronics11162627","journal-title":"Electronics"},{"key":"11452_CR18","doi-asserted-by":"publisher","first-page":"5615","DOI":"10.1109\/TII.2020.3023430","volume":"17","author":"B Li","year":"2021","unstructured":"Li B, Wu Y, Song J, Lu R, Li T, Zhao L (2021) DeepFed: Federated deep learning for intrusion detection in industrial cyber\u2013physical systems IEEE Trans. Ind Inform 17:5615\u20135624. https:\/\/doi.org\/10.1109\/TII.2020.3023430","journal-title":"Ind Inform"},{"key":"11452_CR19","doi-asserted-by":"crossref","unstructured":"Kasinathan P, Pastrone C, Spirito MA, Vinkovits M (2013) Denial-of-service detection in 6lowpan based internet of things. In: 2013 IEEE 9th International conference on wireless and mobile computing, networking and communications, WiMob, IEEE, , pp. 600\u2013607","DOI":"10.1109\/WiMOB.2013.6673419"},{"key":"11452_CR20","first-page":"1","volume":"41","author":"A-H Muna","year":"2018","unstructured":"Muna A-H, Moustafa N, Sitnikova E (2018) Identification of malicious activities in industrial internet of things based on deep learning models. J Inf Secur Appl 41:1\u201311","journal-title":"J Inf Secur Appl"},{"issue":"12","key":"11452_CR21","doi-asserted-by":"publisher","first-page":"24188","DOI":"10.3390\/s141224188","volume":"14","author":"D Oh","year":"2014","unstructured":"Oh D, Kim D, Ro W (2014) A malicious pattern detection engine for embedded security systems in the internet of things. Sensors 14(12):24188\u201324211","journal-title":"Sensors"},{"key":"11452_CR22","doi-asserted-by":"publisher","unstructured":"Evmorfos S, Vlachodimitropoulos G, Bakalos N, Gelenbe E (2020) Neural network architectures for the detection of SYN flood attacks in IoT systems, In: Proceedings of the 13th ACM International conference on PErvasive technologies related to assistive environments, PETRA\u201920, Association for computing machinery, New York, NY, USA, https:\/\/doi.org\/10.1145\/3389189.3398000.","DOI":"10.1145\/3389189.3398000"},{"issue":"16","key":"11452_CR23","doi-asserted-by":"publisher","first-page":"4372","DOI":"10.3390\/s20164372","volume":"20","author":"YN Soe","year":"2020","unstructured":"Soe YN, Feng Y, Santosa PI, Hartanto R, Sakurai K (2020) Machine learning-based IoT-botnet attack detection with sequential architecture. Sensors 20(16):4372. https:\/\/doi.org\/10.3390\/s20164372","journal-title":"Sensors"},{"key":"11452_CR24","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1016\/j.asoc.2018.05.049","volume":"72","author":"S Rathore","year":"2018","unstructured":"Rathore S, Park JH (2018) Semi-supervised learning based distributed attack detection framework for IoT. Appl Soft Comput 72:79\u201389. https:\/\/doi.org\/10.1016\/j.asoc.2018.05.049","journal-title":"Appl Soft Comput"},{"key":"11452_CR25","doi-asserted-by":"crossref","unstructured":"Cho EJ, Kim JH, Hong CS (2009) Attack model and detection scheme for botnet on 6lowpan, In: Asia-Pacific Network Operations and Management Symposium, Springer, pp. 515\u2013518","DOI":"10.1007\/978-3-642-04492-2_66"},{"key":"11452_CR26","doi-asserted-by":"crossref","unstructured":"Thanigaivelan NK, Nigussie E, Kanth RK, Virtanen S, Isoaho J (2016) Distributed internal anomaly detection system for internet-of-things, In: 2016 13th IEEE Annual Consumer Communications & Networking Conference, CCNC, IEEE, , pp. 319\u2013320.","DOI":"10.1109\/CCNC.2016.7444797"},{"key":"11452_CR27","doi-asserted-by":"crossref","unstructured":"Summerville DH, Zach KM, Chen Y (2015) Ultra-lightweight deep packet anomaly detection for internet of things devices, In: 2015 IEEE 34th International Performance Computing and Communications Conference, IPCCC, IEEE, pp. 1\u20138","DOI":"10.1109\/PCCC.2015.7410342"},{"key":"11452_CR28","doi-asserted-by":"crossref","unstructured":"Lee T-H, Wen C-H, Chang L-H, Chiang H-S, Hsieh M-C (2014) A lightweight intrusion detection scheme based on energy consumption analysis in 6Low-PAN, In: Advanced technologies, embedded and multimedia for human-centric computing, Springer, pp. 1205\u20131213","DOI":"10.1007\/978-94-007-7262-5_137"},{"key":"11452_CR29","doi-asserted-by":"crossref","unstructured":"PongleP, Chavan G, (2015) Real time intrusion and wormhole attack detection in internet of things, Int. J. Comput. Appl. 121(9)","DOI":"10.5120\/21565-4589"},{"key":"11452_CR30","doi-asserted-by":"crossref","unstructured":"Zhao S, Li W, Zia T, Zomaya AY (2017) A dimension reduction model and classifier for anomaly-based intrusion detection in internet of things, In: 2017 IEEE 15th Intl Conf on Dependable, Autonomic and Secure Computing, 15th Intl Conf on Pervasive Intelligence and Computing, 3rd Intl Conf on Big Data intelligence and computing and cyber science and technology congress, DASC\/PiCom\/DataCom\/CyberSciTech, IEEE, pp. 836\u2013843","DOI":"10.1109\/DASC-PICom-DataCom-CyberSciTec.2017.141"},{"key":"11452_CR31","unstructured":"Pajouh HH, Javidan R, Khayami R, Ali D, Choo K-KR (2016) A two-layer dimension reduction and two-tier classification model for anomaly-based intrusion detection in IoT backbone networks, IEEE Trans. Emerg. Top. Comput"},{"issue":"March","key":"11452_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2023.108651","volume":"108","author":"X Wang","year":"2023","unstructured":"Wang X, Wang Y, Javaheri Z, Almutairi L, Moghadamnejad N, Younes OS (2023) Federated deep learning for anomaly detection in the internet of things. Comput Electr Eng 108(March):108651. https:\/\/doi.org\/10.1016\/j.compeleceng.2023.108651","journal-title":"Comput Electr Eng"},{"key":"11452_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.121000","author":"MJ Idrissi","year":"2023","unstructured":"Idrissi MJ, Alami H, El Mahdaouy A, El Mekki A, Oualil S, Yartaoui Z, Berrada I (2023) Fed-ANIDS: Federated learning for anomaly-based network intrusion detection systems. Expert Syst Appl. https:\/\/doi.org\/10.1016\/j.eswa.2023.121000","journal-title":"Expert Syst Appl"},{"issue":"3","key":"11452_CR34","doi-asserted-by":"publisher","first-page":"314","DOI":"10.1016\/j.dcan.2022.02.007","volume":"8","author":"B Weinger","year":"2022","unstructured":"Weinger B, Kim J, Sim A, Nakashima M, Moustafa N, Wu KJ (2022) Enhancing IoT anomaly detection performance for federated learning. Digit Commun Netw 8(3):314\u2013323. https:\/\/doi.org\/10.1016\/j.dcan.2022.02.007","journal-title":"Digit Commun Netw"},{"key":"11452_CR35","doi-asserted-by":"publisher","first-page":"2545","DOI":"10.1109\/JIOT.2021.3077803","volume":"9","author":"V Mothukuri","year":"2022","unstructured":"Mothukuri V, Khare P, Parizi RM, Pouriyeh S, Dehghantanha A, Srivastava G (2022) Federated-learning-based anomaly detection for iot security attacks IEEE Int. Things J 9:2545\u20132554. https:\/\/doi.org\/10.1109\/JIOT.2021.3077803","journal-title":"Things J"},{"key":"11452_CR36","doi-asserted-by":"publisher","unstructured":"Chen Z, Lv N, Liu P, Fang Y, Chen K, Pan W (2020) Intrusion detection for wireless edge networks based on federated learning IEEE Access, 8: 217463\u2013217472. https:\/\/doi.org\/10.1109\/ACCESS.2020.3041793","DOI":"10.1109\/ACCESS.2020.3041793"},{"key":"11452_CR37","doi-asserted-by":"publisher","unstructured":"Zhang T, He C, Ma T, Gao L, Ma M, Avestimehr S (2021) Federated learning for Internet of things Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems, Association for Computing Machinery, New York, NY, USA, pp. 413\u2013419, https:\/\/doi.org\/10.1145\/3485730.3493444","DOI":"10.1145\/3485730.3493444"},{"key":"11452_CR38","doi-asserted-by":"publisher","unstructured":"Fan Y, Li Y, Zhan M, Cui H, Zhang Y (2020) Iotdefender: a federated transfer learning intrusion detection framework for 5g iot 2020 IEEE 14th International Conference on Big Data Science and Engineering (BigDataSE), pp. 88\u201395, https:\/\/doi.org\/10.1109\/BigDataSE50710.2020.00020","DOI":"10.1109\/BigDataSE50710.2020.00020"},{"key":"11452_CR39","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1109\/MNET.011.2000286","volume":"34","author":"SA Rahman","year":"2020","unstructured":"Rahman SA, Tout H, Talhi C, Mourad A (2020) Internet of things intrusion detection: centralized, on-device, or federated learning? IEEE Netw 34:310\u2013317. https:\/\/doi.org\/10.1109\/MNET.011.2000286","journal-title":"IEEE Netw"},{"key":"11452_CR40","doi-asserted-by":"publisher","first-page":"6073","DOI":"10.1109\/TVT.2021.3076780","volume":"70","author":"H Liu","year":"2021","unstructured":"Liu H, Zhang S, Zhang P, Zhou X, Shao X, Pu G, Zhang Y (2021) Blockchain and federated learning for collaborative intrusion detection in vehicular edge computing IEEE Trans. Veh Technol 70:6073\u20136084. https:\/\/doi.org\/10.1109\/TVT.2021.3076780","journal-title":"Veh Technol"},{"key":"11452_CR41","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1016\/j.comcom.2015.06.012","volume":"67","author":"P Xiao","year":"2015","unstructured":"Xiao P, Qu W, Qi H, Li Z (2015) Detecting DDoS attacks against data center with correlation analysis. Comput Commun 67:66\u201374","journal-title":"Comput Commun"},{"issue":"1","key":"11452_CR42","first-page":"13","volume":"9","author":"C She","year":"2017","unstructured":"She C, Wen W, Lin Z, Zheng K (2017) Application-layer DDoS detection based on a one-class support vector machine. Int J Netw Secur Appl (IJNSA) 9(1):13\u201324","journal-title":"Int J Netw Secur Appl (IJNSA)"},{"key":"11452_CR43","doi-asserted-by":"crossref","unstructured":"Vishwakarma R, Jain AK (2019) A honeypot with machine learning based detection framework for defending IoT based botnet DDoS attacks. In: 2019 3rd international Conference on trends in electronics and informatics (ICOEI)","DOI":"10.1109\/ICOEI.2019.8862720"},{"key":"11452_CR44","doi-asserted-by":"crossref","unstructured":"Asad M, Asim M, Javed T, Beg MO, Mujtaba H, Abbas S (2019) Deep detect: Detection of distributed denial of service attacks using deep learning. In: The computer journal","DOI":"10.1093\/comjnl\/bxz064"},{"key":"11452_CR45","doi-asserted-by":"crossref","unstructured":"Roopak M, Tian GY, Chambers J (2019) \u201cDeep learning models for cyber security in IoT networks. In: 2019 IEEE 9th annual computing and communication workshop and conference (CCWC), pp. 0452\u20130457","DOI":"10.1109\/CCWC.2019.8666588"},{"key":"11452_CR46","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1109\/MPRV.2018.03367731","volume":"17","author":"Y Meidan","year":"2018","unstructured":"Meidan Y, Bohadana M, Mathov Y, Mirsky Y, Shabtai A, Breitenbacher D, Elovici Y (2018) N-BaIoT\u2014network-based detection of IoT botnet attacks using deep autoencoders. IEEE Pervasive Comput 17:12\u201322. https:\/\/doi.org\/10.1109\/MPRV.2018.03367731","journal-title":"IEEE Pervasive Comput"},{"key":"11452_CR47","doi-asserted-by":"crossref","unstructured":"Doshi R, Apthorpe N, Feamster N (2018) Machine learning DDoS detection for consumer internet of things devices. In: 2018 IEEE security and privacy workshops (SPW)","DOI":"10.1109\/SPW.2018.00013"},{"key":"11452_CR48","doi-asserted-by":"crossref","unstructured":"She C, Wen W, Lin Z, Zheng K (2019) \u201cDad-mcnn: DDoS attack detection via multichannel CNN. In: Proceedings of the 2019 11th international conference on machine learning and computing, pp. 484-488","DOI":"10.1145\/3318299.3318329"},{"issue":"2","key":"11452_CR49","doi-asserted-by":"publisher","first-page":"876","DOI":"10.1109\/TNSM.2020.2971776","volume":"17","author":"R Doriguzzi-Corin","year":"2020","unstructured":"Doriguzzi-Corin R, Millar S, Scott-Hayward S, Mart\u00ednez-del-Rinc\u00f3n J, Siracusa D (2020) Lucid: a practical, lightweight deep learning solution for DDoS attack detection. IEEE Trans Netw Serv Manage 17(2):876\u2013889. https:\/\/doi.org\/10.1109\/TNSM.2020.2971776","journal-title":"IEEE Trans Netw Serv Manage"},{"issue":"10","key":"11452_CR50","doi-asserted-by":"publisher","first-page":"9552","DOI":"10.1109\/JIOT.2020.2993782","volume":"7","author":"Y Jia","year":"2020","unstructured":"Jia Y, Zhong F, Alrawais A, Gong B, Cheng X (2020) FlowGuard: an intelligent edge defense mechanism against IoT DDoS attacks. IEEE Internet Things J 7(10):9552\u20139562. https:\/\/doi.org\/10.1109\/JIOT.2020.2993782","journal-title":"IEEE Internet Things J"},{"key":"11452_CR51","doi-asserted-by":"publisher","unstructured":"Roopak M, Tian GY, Chambers J (2020) An intrusion detection system against DDoS attacks in IoT networks. In: 2020 10th annual computing and communication workshop and conference (CCWC), pp. 0562\u20130567, https:\/\/doi.org\/10.1109\/CCWC47524.2020.9031206","DOI":"10.1109\/CCWC47524.2020.9031206"},{"key":"11452_CR52","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1237\/3\/032040","volume":"1237","author":"J Pei","year":"2019","unstructured":"Pei J, Chen Y, Ji W (2019) A DDoS Attack Detection Method Based on Machine Learning. J Phys: Conf Ser 1237:032040. https:\/\/doi.org\/10.1088\/1742-6596\/1237\/3\/032040","journal-title":"J Phys: Conf Ser"},{"key":"11452_CR53","unstructured":"Yijie, Li, Zhai Shang, Chen Mingrui. (2019) \u201cDDoS attack detection method based on feature extraction of deep belief network.\u201d arXiv: Cryptography and Security"},{"key":"11452_CR54","doi-asserted-by":"crossref","unstructured":"Yuan Xiaoyong, Chuanhuang Li, Xiaolin Li (2017) DeepDefense: identifying DDoS attack via deep learning. In: 2017 IEEE international conference on smart computing (SMARTCOMP) pp. 1\u20138","DOI":"10.1109\/SMARTCOMP.2017.7946998"},{"key":"11452_CR55","doi-asserted-by":"crossref","unstructured":"Kaur G, Prinima G (2019) \u201cHybrid Approach for detecting DDOS Attacks in Software Defined Networks. In: 2019 Twelfth international conference on contemporary computing (IC3) pp. 1\u20136","DOI":"10.1109\/IC3.2019.8844944"},{"key":"11452_CR56","doi-asserted-by":"publisher","first-page":"419","DOI":"10.3103\/S0146411619050043","volume":"53","author":"N Bindra","year":"2019","unstructured":"Bindra N, Sood M (2019) Detecting DDoS attacks using machine learning techniques and contemporary intrusion detection dataset. Autom Control Comput Sci 53:419\u2013428. https:\/\/doi.org\/10.3103\/S0146411619050043","journal-title":"Autom Control Comput Sci"},{"key":"11452_CR57","doi-asserted-by":"crossref","unstructured":"Roempluk T, Olarik S (2019) A machine learning approach for detecting distributed denial of service attacks. In: 2019 Joint international conference on digital arts, media and technology with ECTI Northern section conference on electrical, electronics, computer and telecommunications engineering (ECTI DAMT-NCON) pp. 146\u201314","DOI":"10.1109\/ECTI-NCON.2019.8692243"},{"key":"11452_CR58","doi-asserted-by":"publisher","unstructured":"Kim Y, Hakak S, Ghorbani A (2023) \u201cDDoS attack dataset (CICEV2023) against EV Authentication in Charging Infrastructure. In: 2023 20th annual international conference on privacy, security and trust (PST), Copenhagen, Denmark, pp 1\u20139. https:\/\/doi.org\/10.1109\/PST58708.2023.10320202","DOI":"10.1109\/PST58708.2023.10320202"},{"key":"11452_CR59","unstructured":"https:\/\/www.unb.ca\/cic\/datasets\/iotdataset-2023.html"},{"issue":"2","key":"11452_CR60","first-page":"317","volume":"92","author":"J Mirkovic","year":"2004","unstructured":"Mirkovic J, Reiher P, Shepherd F (2004) Modeling and defending against DDoS attacks. Proc IEEE 92(2):317\u2013331","journal-title":"Proc IEEE"},{"key":"11452_CR61","doi-asserted-by":"publisher","unstructured":"Li Q, Meng L, Zhang Y, Yan J (2019) DDoS attacks detection using machine learning algorithms. https:\/\/doi.org\/10.1007\/978-981-13-8138-6_17.","DOI":"10.1007\/978-981-13-8138-6_17"},{"key":"11452_CR62","doi-asserted-by":"crossref","unstructured":"Soe YN, Paulus IS, Rudy H (2019) \u201cDDoS attack detection based on simple ANN with SMOTE for IoT environment. In: 2019 Fourth international conference on informatics and computing (ICIC) (2019) pp. 1\u20135","DOI":"10.1109\/ICIC47613.2019.8985853"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11452-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-025-11452-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11452-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T07:29:59Z","timestamp":1757316599000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-025-11452-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,30]]},"references-count":62,"journal-issue":{"issue":"25","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["11452"],"URL":"https:\/\/doi.org\/10.1007\/s00521-025-11452-7","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,30]]},"assertion":[{"value":"24 August 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 June 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 July 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflicts of interest to report regarding the present study.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interests"}},{"value":"There is no any ethical conflicts.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}