{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T09:22:17Z","timestamp":1783675337857,"version":"3.55.0"},"reference-count":40,"publisher":"PeerJ","license":[{"start":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T00:00:00Z","timestamp":1770076800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>The widespread adoption of electric vehicles (EVs) is crucial for reducing greenhouse gas emissions, yet it exposes charging infrastructure to sophisticated cyber threats. In the next generation EV charging networks, ultra-low latency, massive device connectivity, and AI-driven automation are key technologies. Traditional intrusion detection systems (IDS) struggle with class imbalance, overfitting, and real-time anomaly detection. These limitations threaten user privacy, service continuity, and grid stability. This study proposes a Variational Auto Encoder (VAE) based XGBoost, a hybrid IDS that leverages VAE for feature extraction and handling imbalanced attack data, while XGBoost improves classification accuracy. Evaluated on the CICEVSE2024 dataset, the model surpasses traditional methods like K-Nearest Neighbors and Random Forest, achieving an accuracy of 88.75%, a precision of 88.73%, a recall of 88.75%, and an F1-score of 88.67%. By integrating AI-driven anomaly detection into 6G-enabled smart grids, VAE-XGBoost enhances the cybersecurity resilience of EV charging infrastructure, ensuring scalability, adaptability to novel threats, and real-time mitigation for sustainable and secure transportation networks.<\/jats:p>","DOI":"10.7717\/peerj-cs.3506","type":"journal-article","created":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T08:14:51Z","timestamp":1770106491000},"page":"e3506","source":"Crossref","is-referenced-by-count":2,"title":["VAE-XGBoost: a hybrid intrusion detection system for next generation EV charging networks"],"prefix":"10.7717","volume":"12","author":[{"given":"Muhammad","family":"Asim","sequence":"first","affiliation":[{"name":"Institute of Computer Sciences and Information Technology, Faculty of Management and Computer Sciences, The University of Agriculture, Peshawar, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Farrukh","family":"Sair","sequence":"additional","affiliation":[{"name":"Institute of Computer Sciences and Information Technology, Faculty of Management and Computer Sciences, The University of Agriculture, Peshawar, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1963-5041","authenticated-orcid":true,"given":"Muhammad","family":"Ishaq","sequence":"additional","affiliation":[{"name":"Institute of Computer Sciences and Information Technology, Faculty of Management and Computer Sciences, The University of Agriculture, Peshawar, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6594-8861","authenticated-orcid":true,"given":"Korhan","family":"Cengiz","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Prince Mohammad Bin Fahd University, Al Khobar, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7005-6489","authenticated-orcid":true,"given":"Sedat","family":"Akleylek","sequence":"additional","affiliation":[{"name":"Institute of Computer Science, University of Tartu, Tartu, Estonia"},{"name":"Department of Computer Engineering, Istinye University, Istanbul, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1730-2518","authenticated-orcid":true,"given":"Nikola","family":"Ivkovi\u0107","sequence":"additional","affiliation":[{"name":"Faculty of Organization and Informatics, University of Zagreb, Vara\u017edin, Croatia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"4443","published-online":{"date-parts":[[2026,2,3]]},"reference":[{"key":"10.7717\/peerj-cs.3506\/ref-1","doi-asserted-by":"publisher","first-page":"214434\u2013214453","DOI":"10.1109\/access.2020.3041074","article-title":"Cybersecurity of smart electric vehicle charging: a power grid perspective","volume":"8","author":"Acharya","year":"2020","journal-title":"IEEE Access"},{"key":"10.7717\/peerj-cs.3506\/ref-2","first-page":"399","article-title":"Security threats in electric vehicle charging","author":"Ahalawat","year":"2022"},{"issue":"4","key":"10.7717\/peerj-cs.3506\/ref-3","doi-asserted-by":"publisher","first-page":"8881","DOI":"10.1109\/tte.2024.3368920","article-title":"Detection of cyber attacks to mitigate their impacts on the manipulated EV charging prices","volume":"10","author":"Akbarian","year":"2024","journal-title":"IEEE Transactions on Transportation Electrification"},{"issue":"7","key":"10.7717\/peerj-cs.3506\/ref-4","doi-asserted-by":"publisher","first-page":"102860","DOI":"10.1016\/j.asej.2024.102860","article-title":"Modeling time-varying wide-scale distributed denial of service attacks on electric vehicle charging stations","volume":"15","author":"Aljohani","year":"2024","journal-title":"Ain Shams Engineering Journal"},{"issue":"447\u2013462","key":"10.7717\/peerj-cs.3506\/ref-5","doi-asserted-by":"publisher","first-page":"869","DOI":"10.1109\/ojvt.2024.3422253","article-title":"Deep learning in the fast lane: a survey on advanced intrusion detection systems for intelligent vehicle networks","volume":"5","author":"Almehdhar","year":"2024","journal-title":"IEEE Open Journal of Vehicular Technology"},{"issue":"6","key":"10.7717\/peerj-cs.3506\/ref-6","doi-asserted-by":"publisher","first-page":"108702","DOI":"10.1016\/j.compeleceng.2023.108702","article-title":"Cyber security and beyond: detecting malware and concept drift in AI-based sensor data streams using statistical techniques","volume":"108","author":"Amin","year":"2023","journal-title":"Computers and Electrical Engineering"},{"issue":"1","key":"10.7717\/peerj-cs.3506\/ref-7","doi-asserted-by":"publisher","first-page":"6","DOI":"10.3390\/jsan11010006","article-title":"Towards a lightweight intrusion detection framework for in-vehicle networks","volume":"11","author":"Basavaraj","year":"2022","journal-title":"Journal of Sensor and Actuator Networks"},{"key":"10.7717\/peerj-cs.3506\/ref-8","doi-asserted-by":"publisher","first-page":"15272","DOI":"10.1109\/access.2025.3529526","article-title":"Temporal convolutional network approach to secure open charge point protocol (OCPP) in electric vehicle charging","volume":"13","author":"Benfarhat","year":"2025a","journal-title":"IEEE Access"},{"key":"10.7717\/peerj-cs.3506\/ref-9","doi-asserted-by":"publisher","first-page":"1033","DOI":"10.1109\/ojvt.2025.3559421","article-title":"Advanced temporal convolutional network framework for intrusion detection in electric vehicle charging stations","volume":"6","author":"Benfarhat","year":"2025b","journal-title":"IEEE Open Journal of Vehicular Technology"},{"key":"10.7717\/peerj-cs.3506\/ref-10","article-title":"EVGen: adversarial networks for learning electric vehicle charging loads and hidden representations","volume-title":"ICML 2021 Workshop on Tackling Climate Change with Machine Learning","author":"Buechler","year":"2021"},{"key":"10.7717\/peerj-cs.3506\/ref-11","first-page":"171","volume-title":"Enhancing EV charging station security using a multi-dimensional dataset: CICEVSE2024","author":"Buedi","year":"2024"},{"issue":"4","key":"10.7717\/peerj-cs.3506\/ref-12","doi-asserted-by":"publisher","first-page":"1044","DOI":"10.3390\/electronics12041044","article-title":"A machine learning-based intrusion detection system for IoT electric vehicle charging stations (EVCSs)","volume":"12","author":"ElKashlan","year":"2023","journal-title":"Electronics"},{"issue":"3","key":"10.7717\/peerj-cs.3506\/ref-13","doi-asserted-by":"publisher","first-page":"2010","DOI":"10.1109\/tte.2020.3044524","article-title":"Cyberattack detection for electric vehicles using physics-guided machine learning","volume":"7","author":"Guo","year":"2020","journal-title":"IEEE Transactions on Transportation Electrification"},{"issue":"18","key":"10.7717\/peerj-cs.3506\/ref-14","doi-asserted-by":"publisher","first-page":"6593","DOI":"10.3390\/app10186593","article-title":"Degradation state recognition of piston pump based on ICEEMDAN and XGBoost","volume":"10","author":"Guo","year":"2020","journal-title":"Applied Sciences"},{"issue":"3","key":"10.7717\/peerj-cs.3506\/ref-15","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1007\/s10207-025-01055-7","article-title":"Cyber defense in OCPP for EV charging security risks","volume":"24","author":"Hamdare","year":"2025","journal-title":"International Journal of Information Security"},{"issue":"15","key":"10.7717\/peerj-cs.3506\/ref-16","doi-asserted-by":"publisher","first-page":"6716","DOI":"10.3390\/s23156716","article-title":"Cybersecurity risk analysis of electric vehicles charging stations","volume":"23","author":"Hamdare","year":"2023","journal-title":"Sensors"},{"issue":"1","key":"10.7717\/peerj-cs.3506\/ref-17","doi-asserted-by":"publisher","first-page":"122181","DOI":"10.1016\/j.eswa.2023.122181","article-title":"Supervised contrastive ResNet and transfer learning for the in-vehicle intrusion detection system","volume":"238","author":"Hoang","year":"2024","journal-title":"Expert Systems with Applications"},{"key":"10.7717\/peerj-cs.3506\/ref-18","doi-asserted-by":"publisher","first-page":"13565","DOI":"10.1109\/access.2018.2812176","article-title":"LNSC: a security model for electric vehicle and charging pile management based on blockchain ecosystem","volume":"6","author":"Huang","year":"2018","journal-title":"IEEE Access"},{"key":"10.7717\/peerj-cs.3506\/ref-19","doi-asserted-by":"publisher","first-page":"55856","DOI":"10.1109\/access.2022.3177842","article-title":"Electric vehicle user data-induced cyber attack on electric vehicle charging station","volume":"10","author":"Jeong","year":"2022","journal-title":"IEEE Access"},{"issue":"4","key":"10.7717\/peerj-cs.3506\/ref-20","doi-asserted-by":"publisher","first-page":"571","DOI":"10.3390\/math12040571","article-title":"Next\u2013generation intrusion detection for IoT EVCs: integrating CNN, LSTM, and GRU models","volume":"12","author":"Kilichev","year":"2024","journal-title":"Mathematics"},{"key":"10.7717\/peerj-cs.3506\/ref-21","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1312.6114","article-title":"Auto-encoding variational bayes","volume-title":"2nd International Conference on Learning Representations, {ICLR} 2014, Banff, AB, Canada, April 14\u201316, 2014, Conference Track Proceedings","author":"Kingma","year":"2014"},{"issue":"1","key":"10.7717\/peerj-cs.3506\/ref-22","doi-asserted-by":"publisher","first-page":"100013","DOI":"10.1016\/j.array.2019.100013","article-title":"A novel intrusion detection system against spoofing attacks in connected electric vehicles","volume":"5","author":"Kosmanos","year":"2020","journal-title":"Array"},{"issue":"14","key":"10.7717\/peerj-cs.3506\/ref-23","doi-asserted-by":"publisher","first-page":"2180","DOI":"10.3390\/electronics11142180","article-title":"Using deep learning networks to identify cyber attacks on intrusion detection for in-vehicle networks","volume":"11","author":"Lin","year":"2022","journal-title":"Electronics"},{"issue":"20","key":"10.7717\/peerj-cs.3506\/ref-24","doi-asserted-by":"publisher","first-page":"e39299","DOI":"10.1016\/j.heliyon.2024.e39299","article-title":"Advancement of electric vehicle technologies, classification of charging methodologies, and optimization strategies for sustainable development\u2014a comprehensive review","volume":"10","author":"Madaram","year":"2024","journal-title":"Heliyon"},{"issue":"14","key":"10.7717\/peerj-cs.3506\/ref-25","doi-asserted-by":"publisher","first-page":"4736","DOI":"10.3390\/s21144736","article-title":"Deep transfer learning based intrusion detection system for electric vehicular networks","volume":"21","author":"Mehedi","year":"2021","journal-title":"Sensors"},{"key":"10.7717\/peerj-cs.3506\/ref-26","doi-asserted-by":"crossref","DOI":"10.24200\/sci.2024.64239.8820","article-title":"Securing vehicle-to-grid networks: a bio-inspired intrusion detection system","author":"Mekkaoui","year":"2024","journal-title":"Scientia Iranica"},{"issue":"22","key":"10.7717\/peerj-cs.3506\/ref-27","doi-asserted-by":"publisher","first-page":"14731","DOI":"10.3390\/su142214731","article-title":"Optimal allocation of fast charging station for integrated electric-transportation system using multi-objective approach","volume":"14","author":"Mohanty","year":"2022","journal-title":"Sustainability"},{"key":"10.7717\/peerj-cs.3506\/ref-28","doi-asserted-by":"publisher","first-page":"99595","DOI":"10.1109\/access.2021.3095962","article-title":"Comparative performance evaluation of intrusion detection based on machine learning in in-vehicle controller area network bus","volume":"9","author":"Moulahi","year":"2021","journal-title":"IEEE Access"},{"key":"10.7717\/peerj-cs.3506\/ref-29","doi-asserted-by":"publisher","first-page":"55389","DOI":"10.1109\/ACCESS.2023.3282110","article-title":"Transformer-based attention network for in-vehicle intrusion detection","volume":"11","author":"Nguyen","year":"2023","journal-title":"IEEE Access"},{"issue":"8","key":"10.7717\/peerj-cs.3506\/ref-30","doi-asserted-by":"publisher","first-page":"1757","DOI":"10.3390\/electronics12081757","article-title":"An intelligent intrusion detection system for 5G-enabled internet of vehicles","volume":"12","author":"Sousa","year":"2023","journal-title":"Electronics"},{"issue":"4","key":"10.7717\/peerj-cs.3506\/ref-31","doi-asserted-by":"publisher","first-page":"100827","DOI":"10.1016\/j.measen.2023.100827","article-title":"A comprehensive review of AI based intrusion detection system","volume":"28","author":"Sowmya","year":"2023","journal-title":"Measurement: Sensors"},{"key":"10.7717\/peerj-cs.3506\/ref-32","doi-asserted-by":"publisher","first-page":"82288","DOI":"10.1109\/access.2021.3087113","article-title":"Towards enhancing spectrum sensing: signal classification using autoencoders","volume":"9","author":"Subray","year":"2021","journal-title":"IEEE Access"},{"issue":"7","key":"10.7717\/peerj-cs.3506\/ref-33","doi-asserted-by":"publisher","first-page":"43","DOI":"10.14445\/22315381\/IJETT-V70I7P205","article-title":"A review on analysis of k-nearest neighbor classification machine learning algorithms based on supervised learning","volume":"70","author":"Suyal","year":"2022","journal-title":"International Journal of Engineering Trends and Technology"},{"key":"10.7717\/peerj-cs.3506\/ref-34","doi-asserted-by":"publisher","first-page":"42431","DOI":"10.1109\/access.2023.3271287","article-title":"Battery degradation in electric and hybrid electric vehicles: a survey study","volume":"11","author":"Timilsina","year":"2023","journal-title":"IEEE Access"},{"issue":"12","key":"10.7717\/peerj-cs.3506\/ref-35","doi-asserted-by":"publisher","first-page":"71566","DOI":"10.1109\/access.2024.3400913","article-title":"Securing electric vehicle performance: machine learning-driven fault detection and classification","volume":"12","author":"Ul Islam Khan","year":"2024","journal-title":"IEEE Access"},{"issue":"6","key":"10.7717\/peerj-cs.3506\/ref-36","doi-asserted-by":"publisher","first-page":"6603","DOI":"10.1109\/tia.2019.2936474","article-title":"Electrical safety considerations in large-scale electric vehicle charging stations","volume":"55","author":"Wang","year":"2019","journal-title":"IEEE Transactions on Industry Applications"},{"issue":"1","key":"10.7717\/peerj-cs.3506\/ref-37","doi-asserted-by":"publisher","first-page":"100043","DOI":"10.1016\/j.geits.2022.100043","article-title":"Composites for electric vehicles and automotive sector: a review","volume":"2","author":"Wazeer","year":"2023","journal-title":"Green Energy and Intelligent Transportation"},{"issue":"1","key":"10.7717\/peerj-cs.3506\/ref-38","doi-asserted-by":"publisher","first-page":"616","DOI":"10.1109\/jiot.2021.3084796","article-title":"MTH-IDS: a multitiered hybrid intrusion detection system for internet of vehicles","volume":"9","author":"Yang","year":"2021","journal-title":"IEEE Internet of Things Journal"},{"issue":"91","key":"10.7717\/peerj-cs.3506\/ref-39","doi-asserted-by":"publisher","first-page":"10852","DOI":"10.1109\/access.2022.3145007","article-title":"A hybrid approach toward efficient and accurate intrusion detection for in-vehicle networks","volume":"10","author":"Zhang","year":"2022","journal-title":"IEEE Access"},{"issue":"6","key":"10.7717\/peerj-cs.3506\/ref-40","doi-asserted-by":"publisher","first-page":"1382","DOI":"10.3390\/en13061382","article-title":"A self-learning detection method of sybil attack based on LSTM for electric vehicles","volume":"13","author":"Zhang","year":"2020","journal-title":"Energies"}],"container-title":["PeerJ Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/peerj.com\/articles\/cs-3506.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/peerj.com\/articles\/cs-3506.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/peerj.com\/articles\/cs-3506.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/peerj.com\/articles\/cs-3506.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T08:14:56Z","timestamp":1770106496000},"score":1,"resource":{"primary":{"URL":"https:\/\/peerj.com\/articles\/cs-3506"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,3]]},"references-count":40,"alternative-id":["10.7717\/peerj-cs.3506"],"URL":"https:\/\/doi.org\/10.7717\/peerj-cs.3506","archive":["CLOCKSS","LOCKSS","Portico"],"relation":{},"ISSN":["2376-5992"],"issn-type":[{"value":"2376-5992","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,3]]},"article-number":"e3506"}}