{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T16:01:29Z","timestamp":1774540889207,"version":"3.50.1"},"reference-count":31,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T00:00:00Z","timestamp":1759190400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Security and Privacy"],"published-print":{"date-parts":[[2025,11]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Vehicular Ad Hoc Networks (VANETs) play a pivotal role in enabling intelligent transportation systems, yet their decentralized and dynamic nature exposes them to a wide range of cyber threats, including Sybil attacks, black hole attacks, replay, and message spoofing. To address these vulnerabilities, we propose HyDra\u2010VANET, a novel hybrid security framework that integrates deep learning, federated learning, and homomorphic encryption for robust and privacy\u2010preserving intrusion detection. At the vehicle level, a convolutional\u2013recurrent neural network (CRNN) is employed to extract both spatial and temporal patterns from real\u2010time vehicular communication and telemetry data, ensuring accurate anomaly detection. Federated learning coordinates decentralized model training across vehicles, enabling collaborative intelligence while eliminating the need to share raw data. To further enhance privacy, a lightweight lattice\u2010based homomorphic encryption scheme allows encrypted inference and secure aggregation, preventing sensitive information leakage at intermediate nodes such as roadside units. Experimental evaluation using multiple datasets and adversarial scenarios demonstrates that HyDra\u2010VANET significantly outperforms baseline intrusion detection systems in detection accuracy, resilience to adversarial manipulation, scalability, and communication efficiency.<\/jats:p>","DOI":"10.1002\/spy2.70109","type":"journal-article","created":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T10:25:13Z","timestamp":1759227913000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Enhancing VANET Security Using a Hybrid Model of Deep Learning and Homomorphic Encryption"],"prefix":"10.1002","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3340-6165","authenticated-orcid":false,"given":"Haythem","family":"Hayouni","sequence":"first","affiliation":[{"name":"IT Department Higher Institute of Computer Science of Kef, University of Jendouba  Jendouba Tunisia"}]}],"member":"311","published-online":{"date-parts":[[2025,9,30]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/MITS.2024.3519620"},{"key":"e_1_2_9_3_1","unstructured":"S. I.Ahsan P.Legg andS. M. I.Alam \u201cPrivacy\u2010Preserving Intrusion Detection in Software\u2010Defined VANET Using Federated Learning With BERT \u201d (2024) https:\/\/doi.org\/10.48550\/arXiv.2401.07343."},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.3390\/s23218772"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/SCEECS64059.2025.10941296"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/s42979\u2010024\u201003465\u20101"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.3390\/sym17050722"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.3390\/electronics13122372"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/s44290\u2010025\u201000256\u20102"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598\u2010025\u201096303\u20100"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2025.3456123"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598\u2010024\u201082313\u2010x"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.3390\/wevj16060324"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2024.103881"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.csa.2025.100090"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.3390\/cryptography9020041"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.1049\/ise2\/4632786"},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598\u2010025\u201014341\u20100"},{"key":"e_1_2_9_19_1","unstructured":"W.Jin Y.Yao S.Han et al. \u201cFedML\u2010HE: An Efficient Homomorphic\u2010Encryption\u2010Based Privacy\u2010Preserving Federated Learning System \u201d(2024) https:\/\/doi.org\/10.48550\/arXiv.2303.10837."},{"key":"e_1_2_9_20_1","doi-asserted-by":"publisher","DOI":"10.3390\/telecom6030048"},{"key":"e_1_2_9_21_1","doi-asserted-by":"publisher","DOI":"10.3390\/electronics12040894"},{"key":"e_1_2_9_22_1","doi-asserted-by":"crossref","unstructured":"M.Garba \u201cPrivacy\u2010Preserving Intrusion Detection Systems in VANETs Using Federated Deep Learning \u201d SSRN(2025) https:\/\/doi.org\/10.2139\/ssrn.5325808.","DOI":"10.2139\/ssrn.5325808"},{"key":"e_1_2_9_23_1","doi-asserted-by":"publisher","DOI":"10.1111\/exsy.13103"},{"key":"e_1_2_9_24_1","volume-title":"NSL\u2010KDD Intrusion Detection Dataset","author":"Nazir H. K.","year":"2017"},{"key":"e_1_2_9_25_1","volume-title":"CICIDS 2020: Intrusion Detection Evaluation Dataset","author":"Canadian Institute for Cybersecurity (CIC)","year":"2020"},{"key":"e_1_2_9_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3264437.3264479"},{"key":"e_1_2_9_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICRITO.2018.8748522"},{"key":"e_1_2_9_28_1","unstructured":"R.Saghir T.Karunathilake andA.F\u00f6rster \u201cComparative Study of Simulators for Vehicular Networks \u201d(2024) https:\/\/doi.org\/10.48550\/arXiv.2403.00546."},{"key":"e_1_2_9_29_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-70604-3_5"},{"key":"e_1_2_9_30_1","doi-asserted-by":"publisher","DOI":"10.4018\/IJSST.333852"},{"key":"e_1_2_9_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2015.2492981"},{"key":"e_1_2_9_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2021.3099488"}],"container-title":["SECURITY AND PRIVACY"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/spy2.70109","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T07:43:19Z","timestamp":1763451799000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/spy2.70109"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,30]]},"references-count":31,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025,11]]}},"alternative-id":["10.1002\/spy2.70109"],"URL":"https:\/\/doi.org\/10.1002\/spy2.70109","archive":["Portico"],"relation":{},"ISSN":["2475-6725","2475-6725"],"issn-type":[{"value":"2475-6725","type":"print"},{"value":"2475-6725","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,30]]},"assertion":[{"value":"2025-08-24","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-09-13","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-09-30","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70109"}}