{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T16:35:19Z","timestamp":1781109319768,"version":"3.54.1"},"reference-count":37,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2023,10,27]],"date-time":"2023-10-27T00:00:00Z","timestamp":1698364800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"B11 unit of assessment, Centre for Computing and Informatics Research Centre, Department of Computer Science, Nottingham Trent University, UK"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Vehicle malfunctions have a direct impact on both human and road safety, making vehicle network security an important and critical challenge. Vehicular ad hoc networks (VANETs) have grown to be indispensable in recent years for enabling intelligent transport systems, guaranteeing traffic safety, and averting collisions. However, because of numerous types of assaults, such as Distributed Denial of Service (DDoS) and Denial of Service (DoS), VANETs have significant difficulties. A powerful Network Intrusion Detection System (NIDS) powered by Artificial Intelligence (AI) is required to overcome these security issues. This research presents an innovative method for creating an AI-based NIDS that uses Deep Learning methods. The suggested model specifically incorporates the Self Attention-Based Bidirectional Long Short-Term Memory (SA-BiLSTM) for classification and the Cascaded Convolution Neural Network (CCNN) for learning high-level features. The Multi-variant Gradient-Based Optimization algorithm (MV-GBO) is applied to improve CCNN and SA-BiLSTM further to enhance the model\u2019s performance. Additionally, information gained using MV-GBO-based feature extraction is employed to enhance feature learning. The effectiveness of the proposed model is evaluated on reliable datasets such as KDD-CUP99, ToN-IoT, and VeReMi, which are utilized on the MATLAB platform. The proposed model achieved 99% accuracy on all the datasets.<\/jats:p>","DOI":"10.3390\/s23218772","type":"journal-article","created":{"date-parts":[[2023,10,27]],"date-time":"2023-10-27T11:50:18Z","timestamp":1698407418000},"page":"8772","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":61,"title":["Vehicular Network Intrusion Detection Using a Cascaded Deep Learning Approach with Multi-Variant Metaheuristic"],"prefix":"10.3390","volume":"23","author":[{"given":"Ankit","family":"Manderna","sequence":"first","affiliation":[{"name":"School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi 110067, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9113-2890","authenticated-orcid":false,"given":"Sushil","family":"Kumar","sequence":"additional","affiliation":[{"name":"School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi 110067, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1610-064X","authenticated-orcid":false,"given":"Upasana","family":"Dohare","sequence":"additional","affiliation":[{"name":"School of Computing Science & Engineering, Galgotias University, Greater Noida 203201, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9486-3533","authenticated-orcid":false,"given":"Mohammad","family":"Aljaidi","sequence":"additional","affiliation":[{"name":"Computer Science Department, Faculty of Information Technology, Zarqa University, Zarqa 13110, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9669-8244","authenticated-orcid":false,"given":"Omprakash","family":"Kaiwartya","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Nottingham Trent University, Nottingham NG11 8NS, UK"},{"name":"Computing and Informatics Research Centre, Nottingham Trent University, Nottingham NG11 8NS, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0862-0533","authenticated-orcid":false,"given":"Jaime","family":"Lloret","sequence":"additional","affiliation":[{"name":"Instituto de Investigaci\u00f3n para la gesti\u00f3n Integrada de Zonas Costeras, Universitat Polit\u00e9cnica de Valencia, Camino Vera s\/n, 46022 Valencia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1007\/s00607-021-01001-0","article-title":"A hybrid machine learning model for intrusion detection in VANET","volume":"104","author":"Bangui","year":"2021","journal-title":"Computing"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"5916","DOI":"10.1109\/JIOT.2018.2872474","article-title":"Cybersecurity Measures for Geocasting in Vehicular Cyber Physical System Environments","volume":"6","author":"Kumar","year":"2018","journal-title":"IEEE Internet Things J."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Kumar, S., Rathore, R.S., Dohare, U., Kaiwartya, O., Lloret, J., and Kumar, N. (2023). BEET: Blockchain Enabled Energy Trading for E-Mobility Oriented Electric Vehicles. IEEE Trans. Mob. Comput., 1\u201317.","DOI":"10.1109\/TMC.2023.3267565"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"113311","DOI":"10.1109\/ACCESS.2019.2934632","article-title":"Delimitated Anti Jammer Scheme for Internet of Vehicle: Machine Learning Based Security Approach","volume":"7","author":"Kumar","year":"2019","journal-title":"IEEE Access"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Rani, R., Kumar, S., Kaiwartya, O., Khasawneh, A.M., Lloret, J., Al-Khasawneh, M.A., Mahmoud, M., and Alarood, A.A. (2021). Towards Green Computing Oriented Security: A Lightweight Postquantum Signature for IoE. Sensors, 21.","DOI":"10.3390\/s21051883"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"23906","DOI":"10.1109\/TITS.2022.3190432","article-title":"LSTM-Based Intrusion Detection System for VANETs: A Time Series Classification Approach to False Message Detection","volume":"23","author":"Yu","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"712","DOI":"10.1016\/j.asoc.2018.12.001","article-title":"A novel Intrusion Detection System for Vehicular Ad Hoc Networks (VANETs) based on differences of traffic flow and position","volume":"75","author":"Liang","year":"2018","journal-title":"Appl. Soft Comput."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"142206","DOI":"10.1109\/ACCESS.2021.3120626","article-title":"Intrusion Detection System Using Machine Learning for Vehicular Ad Hoc Networks Based on ToN-IoT Dataset","volume":"9","author":"Gad","year":"2021","journal-title":"IEEE Access"},{"key":"ref_9","unstructured":"Ben Rabah, N., and Idoudi, H. (2022). Emerging Trends in Cybersecurity Applications, Springer International Publishing."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"154560","DOI":"10.1109\/ACCESS.2019.2948382","article-title":"A Distributed Network Intrusion Detection System for Distributed Denial of Service Attacks in Vehicular Ad Hoc Network","volume":"7","author":"Gao","year":"2019","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"6113","DOI":"10.1007\/s12652-021-02963-x","article-title":"Machine Learning-driven optimization for SVM-based intrusion detection system in vehicular ad hoc networks","volume":"14","author":"Alsarhan","year":"2021","journal-title":"J. Ambient. Intell. Humaniz. Comput."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Chougule, A., Kohli, V., Chamola, V., and Yu, F.R. (2022). Multibranch Reconstruction Error (MbRE) Intrusion Detection Architecture for Intelligent Edge-Based Policing in Vehicular Ad-Hoc Networks. IEEE Trans. Intell. Transp. Syst., 1\u201310.","DOI":"10.1109\/TITS.2022.3201548"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3800","DOI":"10.1109\/TVT.2018.2796242","article-title":"Geometry-Based Localization for GPS Outage in Vehicular Cyber Physical Systems","volume":"67","author":"Kaiwartya","year":"2018","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1016\/j.knosys.2018.09.022","article-title":"A filter model for intrusion detection system in Vehicle Ad Hoc Networks: A hidden Markov methodology","volume":"163","author":"Liang","year":"2018","journal-title":"Knowl.-Based Syst."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3129","DOI":"10.1007\/s11277-020-07797-y","article-title":"Machine Learning-Driven Optimization for Intrusion Detection in Smart Vehicular Networks","volume":"117","author":"Alsarhan","year":"2020","journal-title":"Wirel. Pers. Commun."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Schmidt, D.A., Khan, M.S., and Bennett, B.T. (2019, January 11\u201314). Spline Based Intrusion Detection in Vehicular Ad Hoc Networks (VANET). Proceedings of the SoutheastCon 2019, Huntsville, AL, USA.","DOI":"10.1109\/SoutheastCon42311.2019.9020367"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"107174","DOI":"10.1016\/j.comnet.2020.107174","article-title":"Distributed collaborative intrusion detection system for vehicular Ad Hoc networks based on invariant","volume":"172","author":"Zhou","year":"2020","journal-title":"Comput. Netw."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2707","DOI":"10.1109\/TITS.2019.2905415","article-title":"A Filter Model Based on Hidden Generalized Mixture Transition Distribution Model for Intrusion Detection System in Vehicle Ad Hoc Networks","volume":"21","author":"Liang","year":"2019","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"12724","DOI":"10.1109\/TITS.2021.3117028","article-title":"GaDQN-IDS: A Novel Self-Adaptive IDS for VANETs Based on Bayesian Game Theory and Deep Reinforcement Learning","volume":"23","author":"Liang","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"12013","DOI":"10.1109\/TVT.2021.3113807","article-title":"DeepADV: A Deep Neural Network Framework for Anomaly Detection in VANETs","volume":"70","author":"Alladi","year":"2021","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1893","DOI":"10.1109\/ACCESS.2021.3136706","article-title":"Misbehavior Detection for Position Falsification Attacks in VANETs Using Machine Learning","volume":"10","author":"Ercan","year":"2021","journal-title":"IEEE Access"},{"key":"ref_22","first-page":"148","article-title":"Distributed Privacy-Preserving Collaborative Intrusion Detection Systems for VANETs","volume":"4","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Signal Inf. Process. Netw."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"4385","DOI":"10.1109\/TITS.2020.3036071","article-title":"SP-CIDS: Secure and Private Collaborative IDS for VANETs","volume":"22","author":"Raja","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1109\/MCE.2021.3138703","article-title":"Intrusion Detection System-Based Security Mechanism for Vehicular Ad-Hoc Networks for Industrial IoT","volume":"11","author":"Singh","year":"2021","journal-title":"IEEE Consum. Electron. Mag."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"5167","DOI":"10.1109\/TVT.2022.3225524","article-title":"Blockchain Based Optimized Energy Trading for E-Mobility Using Quantum Reinforcement Learning","volume":"72","author":"Kumar","year":"2023","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_26","doi-asserted-by":"crossref","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":"2019","journal-title":"Array"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"101842","DOI":"10.1016\/j.adhoc.2019.02.001","article-title":"An intrusion detection system for connected vehicles in smart cities","volume":"90","author":"Aloqaily","year":"2019","journal-title":"Ad Hoc Netw."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1016\/j.future.2021.06.029","article-title":"MCFT-CNN: Malware classification with fine-tune convolution neural networks using traditional and transfer learning in Internet of Things","volume":"125","author":"Sudhakar","year":"2021","journal-title":"Futur. Gener. Comput. Syst."},{"key":"ref_29","unstructured":"(2023, July 10). Dataset1. Available online: https:\/\/www.kaggle.com\/datasets\/galaxyh\/kdd-cup-1999-data."},{"key":"ref_30","unstructured":"(2023, July 10). Dataset2. Available online: https:\/\/research.unsw.edu.au\/projects\/toniot-datasets."},{"key":"ref_31","unstructured":"(2023, September 29). Dataset3. Available online: https:\/\/github.com\/josephkamel\/VeReMi-Dataset."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Jiawei, D., Kai, Y., Zhentao, H., Lingjie, J., Lei, H., and Haixia, Y. (2022, January 3\u20145). Research on Intrusion Detection Algorithm Based on Optimized CNN-LSTM. Proceedings of the 2022 International Conference on Networking and Network Applications (NaNA), Urumqi, China.","DOI":"10.1109\/NaNA56854.2022.00024"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Henry, A., Gautam, S., Khanna, S., Rabie, K., Shongwe, T., Bhattacharya, P., Sharma, B., and Chowdhury, S. (2023). Composition of hybrid deep learning model and feature optimization for intrusion detection system. Sensors, 23.","DOI":"10.3390\/s23020890"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Lilhore, U.K., Manoharan, P., Simaiya, S., Alroobaea, R., Alsafyani, M., Baqasah, A.M., Dalal, S., Sharma, A., and Raahemifar, K. (2023). HIDM: Hybrid Intrusion Detection Model for Industry 4.0 Networks Using an Optimized CNN-LSTM with Transfer Learning. Sensors, 23.","DOI":"10.3390\/s23187856"},{"key":"ref_35","first-page":"562","article-title":"Cache agent-based geocasting in VANETs","volume":"7","author":"Kaiwartya","year":"2015","journal-title":"Int. J. Inf. Commun. Technol."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Alladi, T., Agrawal, A., Gera, B., Chamola, V., Sikdar, B., and Guizani, M. (2021, January 14\u201323). Deep Neural Networks for Securing IoT Enabled Vehicular Ad-Hoc Networks. Proceedings of the ICC 2021\u2014IEEE International Conference on Communications, Montreal, QC, Canada.","DOI":"10.1109\/ICC42927.2021.9500823"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1109\/LNET.2021.3058292","article-title":"Securing the Internet of vehicles: A deep learning-based classification framework","volume":"3","author":"Alladi","year":"2021","journal-title":"IEEE Netw. Lett."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/21\/8772\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:12:58Z","timestamp":1760130778000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/21\/8772"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,27]]},"references-count":37,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2023,11]]}},"alternative-id":["s23218772"],"URL":"https:\/\/doi.org\/10.3390\/s23218772","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,27]]}}}