{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T05:39:25Z","timestamp":1775453965348,"version":"3.50.1"},"reference-count":53,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2024,9,24]],"date-time":"2024-09-24T00:00:00Z","timestamp":1727136000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Scientific Research at Northern Border University, Arar, KSA","award":["NBU-FFR-2024-1662-01"],"award-info":[{"award-number":["NBU-FFR-2024-1662-01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>The advent of the Fourth Industrial Revolution has positioned the Internet of Things as a pivotal force in intelligent vehicles. With the source of vehicle-to-everything (V2X), Internet of Things (IoT) networks, and inter-vehicle communication, intelligent connected vehicles are at the forefront of this transformation, leading to complex vehicular networks that are crucial yet susceptible to cyber threats. The complexity and openness of these networks expose them to a plethora of cyber-attacks, from passive eavesdropping to active disruptions like Denial of Service and Sybil attacks. These not only compromise the safety and efficiency of vehicular networks but also pose a significant risk to the stability and resilience of the Internet of Vehicles. Addressing these vulnerabilities, this paper proposes a Dynamic Forest-Structured Ensemble Network (DFSENet) specifically tailored for the Internet of Vehicles (IoV). By leveraging data-balancing techniques and dimensionality reduction, the DFSENet model is designed to detect a wide range of cyber threats effectively. The proposed model demonstrates high efficacy, with an accuracy of 99.2% on the CICIDS dataset and 98% on the car-hacking dataset. The precision, recall, and f-measure metrics stand at 95.6%, 98.8%, and 96.9%, respectively, establishing the DFSENet model as a robust solution for securing the IoV against cyber-attacks.<\/jats:p>","DOI":"10.3390\/info15100583","type":"journal-article","created":{"date-parts":[[2024,9,24]],"date-time":"2024-09-24T08:56:06Z","timestamp":1727168166000},"page":"583","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Smart Collaborative Intrusion Detection System for Securing Vehicular Networks Using Ensemble Machine Learning Model"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4644-1835","authenticated-orcid":false,"given":"Mostafa Mahmoud","family":"El-Gayar","sequence":"first","affiliation":[{"name":"Department of Information Technology, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt"},{"name":"Department of Computer Science, Arab East Colleges, Riyadh 11583, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5957-6467","authenticated-orcid":false,"given":"Faheed A. F.","family":"Alrslani","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Faculty of Computing and Information Technology, Northern Border University, Rafha 91911, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9705-1477","authenticated-orcid":false,"given":"Shaker","family":"El-Sappagh","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science and Engineering, Galala University, Suez 435611, Egypt"},{"name":"Information Systems Department, Faculty of Computers and Artificial Intelligence, Benha University, Banha 13518, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Manale, B., and Tomader, M. (April, January 31). A Survey of Intrusion Detection Algorithm in VANET. Proceedings of the NISS2020: The 3rd International Conference on Networking, Information Systems & Security, ACM International Conference Proceeding Series, Marrakech, Morocco.","DOI":"10.1145\/3386723.3387830"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1153","DOI":"10.1109\/COMST.2015.2494502","article-title":"A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection","volume":"18","author":"Buczak","year":"2016","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1109\/MDAT.2019.2899062","article-title":"Survey of automotive controller area network intrusion detection systems","volume":"36","author":"Young","year":"2019","journal-title":"IEEE Des. Test"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Liang, J., Sheikh, M.S., and Wang, W. (2019). A Survey of Security Services, Attacks, and Applications for Vehicular Ad Hoc Networks (VANETs). Sensors, 19.","DOI":"10.3390\/s19163589"},{"key":"ref_5","first-page":"138","article-title":"A survey on Intrusion Detection Systems and Honeypot based proactive security mechanisms in VANETs and VANET Cloud","volume":"12","author":"Sharma","year":"2018","journal-title":"Veh. Commun."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"13573","DOI":"10.1109\/TITS.2023.3297527","article-title":"A Comprehensive Survey on Authentication and Attack Detection Schemes That Threaten It in Vehicular Ad-Hoc Networks","volume":"24","author":"Dong","year":"2023","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Ullah, S., Khan, M.A., Ahmad, J., Jamal, S.S., e Huma, Z., Hassan, M.T., Pitropakis, N., and Buchanan, W.J. (2022). HDL-IDS: A Hybrid Deep Learning Architecture for Intrusion Detection in the Internet of Vehicles. Sensors, 22.","DOI":"10.3390\/s22041340"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1803","DOI":"10.1109\/TNSM.2020.3014929","article-title":"Multi-Stage Optimized Machine Learning Framework for Network Intrusion Detection","volume":"18","author":"Injadat","year":"2021","journal-title":"IEEE Trans. Netw. Serv. Manag."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1109\/MCI.2012.2228600","article-title":"Ensemble Methods: Foundations and Algorithms [Book Review]","volume":"8","author":"Schwenker","year":"2013","journal-title":"IEEE Comput. Intell. Mag."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Kachirski, O., and Guha, R. (2003, January 6\u20139). Effective intrusion detection using multiple sensors in wireless ad hoc networks. Proceedings of the 36th Annual Hawaii International Conference on System Sciences, HICSS 2003, Big Island, HI, USA.","DOI":"10.1109\/HICSS.2003.1173873"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"e820","DOI":"10.7717\/peerj-cs.820","article-title":"Network intrusion detection using oversampling technique and machine learning algorithms","volume":"8","author":"Ahmed","year":"2022","journal-title":"PeerJ Comput. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Manderna, A., Kumar, S., Dohare, U., Aljaidi, M., Kaiwartya, O., and Lloret, J. (2023). Vehicular Network Intrusion Detection Using a Cascaded Deep Learning Approach with Multi-Variant Metaheuristic. Sensors, 23.","DOI":"10.3390\/s23218772"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Goncalves, F., Ribeiro, B., Gama, O., Santos, A., Costa, A., Dias, B., Macedo, J., and Nicolau, M.J. (2019, January 28\u201330). A Systematic Review on Intelligent Intrusion Detection Systems for VANETs. Proceedings of the 2019 11th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops, Dublin, Ireland.","DOI":"10.1109\/ICUMT48472.2019.8970942"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2219","DOI":"10.1109\/TNSE.2020.2990984","article-title":"Data-Driven Intrusion Detection for Intelligent Internet of Vehicles: A Deep Convolutional Neural Network-Based Method","volume":"7","author":"Nie","year":"2020","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"key":"ref_15","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_16","doi-asserted-by":"crossref","unstructured":"Arya, M., Sastry, H., Dewangan, B.K., Rahmani, M.K.I., Bhatia, S., Muzaffar, A.W., and Bivi, M.A. (2023). Intruder Detection in VANET Data Streams Using Federated Learning for Smart City Environments. Electronics, 12.","DOI":"10.3390\/electronics12040894"},{"key":"ref_17","first-page":"5069104","article-title":"Intelligent Intrusion Detection System for VANET Using Machine Learning and Deep Learning Approaches","volume":"2022","author":"Karthiga","year":"2021","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"e1440","DOI":"10.7717\/peerj-cs.1440","article-title":"Achieving model explainability for intrusion detection in VANETs with LIME","volume":"9","author":"Hassan","year":"2023","journal-title":"PeerJ Comput. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Koscher, K., Czeskis, A., Roesner, F., Patel, S., Kohno, T., Checkoway, S., McCoy, D., Kantor, B., Anderson, D., and Shacham, H. (2010, January 16\u201319). Experimental security analysis of a modern automobile. Proceedings of the 2010 IEEE Symposium on Security and Privacy, Oakland, CA, USA.","DOI":"10.1109\/SP.2010.34"},{"key":"ref_20","unstructured":"Song, W., Choi, H., Kim, J., Kim, E., Kim, Y., and Kim, J. (2024, January 17). Fingerprinting Electronic Control Units for Vehicle Intrusion Detection. Available online: https:\/\/www.usenix.org\/conference\/usenixsecurity16\/technical-sessions\/presentation\/song."},{"key":"ref_21","first-page":"993","article-title":"A Practical Wireless Attack on the Connected Car and Security Protocol for In-Vehicle CAN","volume":"16","author":"Woo","year":"2015","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Kumar, M., Hanumanthappa, M., and Kumar, T.V.S. (2012, January 9\u201311). Intrusion Detection System using decision tree algorithm. Proceedings of the International Conference on Communication Technology Proceedings, ICCT 2012, Chengdu, China.","DOI":"10.1109\/ICCT.2012.6511281"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"4680867","DOI":"10.1155\/2018\/4680867","article-title":"Intrusion Detection System Based on Decision Tree over Big Data in Fog Environment","volume":"2018","author":"Peng","year":"2017","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/j.cose.2014.06.006","article-title":"Selection of Candidate Support Vectors in incremental SVM for network intrusion detection","volume":"45","author":"Chitrakar","year":"2014","journal-title":"Comput. Secur."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhang, H., Dai, S., Li, Y., and Zhang, W. (2018, January 17\u201319). Real-time Distributed-Random-Forest-Based Network Intrusion Detection System Using Apache Spark. Proceedings of the 2018 IEEE 37th International Performance Computing and Communications Conference, IPCCC 2018, Orlando, FL, USA.","DOI":"10.1109\/PCCC.2018.8711068"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Waskle, S., Parashar, L., and Singh, U. (2020, January 2\u20134). Intrusion Detection System Using PCA with Random Forest Approach. Proceedings of the International Conference on Electronics and Sustainable Communication Systems, ICESC 2020, Coimbatore, India.","DOI":"10.1109\/ICESC48915.2020.9155656"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1109\/MCOM.2018.1701270","article-title":"Leveraging LSTM Networks for Attack Detection in Fog-to-Things Communications","volume":"56","author":"Diro","year":"2018","journal-title":"IEEE Commun. Mag."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"74571","DOI":"10.1109\/ACCESS.2020.2988854","article-title":"Fog-Based Attack Detection Framework for Internet of Things Using Deep Learning","volume":"8","author":"Samy","year":"2020","journal-title":"IEEE Access"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1007\/s11277-022-09548-7","article-title":"Fog Computing-Based Intrusion Detection Architecture to Protect IoT Networks","volume":"125","author":"Labiod","year":"2022","journal-title":"Wirel. Pers. Commun."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Li, S., Lu, Y., and Li, J. (2022, January 4\u20136). CAD-IDS: A Cooperative Adaptive Distributed Intrusion Detection System with Fog Computing. Proceedings of the 2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022, Hangzhou, China.","DOI":"10.1109\/CSCWD54268.2022.9776147"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"6435","DOI":"10.1109\/TII.2021.3130248","article-title":"Intrusion Detection Framework for the Internet of Things Using a Dense Random Neural Network","volume":"18","author":"Latif","year":"2022","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2707","DOI":"10.1007\/s11277-021-08359-6","article-title":"Malicious Traffic classification Using Long Short-Term Memory (LSTM) Model","volume":"119","author":"Thapa","year":"2021","journal-title":"Wirel. Pers. Commun."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Latif, S., Driss, M., Boulila, W., e Huma, Z., Jamal, S.S., Idrees, Z., and Ahmad, J. (2021). Deep Learning for the Industrial Internet of Things (IIoT): A Comprehensive Survey of Techniques, Implementation Frameworks, Potential Applications, and Future Directions. Sensors, 21.","DOI":"10.3390\/s21227518"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"7094","DOI":"10.1007\/s10489-021-02205-9","article-title":"Unsupervised deep learning approach for network intrusion detection combining convolutional autoencoder and one-class SVM","volume":"51","author":"Binbusayyis","year":"2021","journal-title":"Appl. Intell."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/978-981-10-3187-8_13","article-title":"A hybrid methodologies for intrusion detection based deep neural network with support vector machine and clustering technique","volume":"422","author":"Ma","year":"2018","journal-title":"Lect. Notes Electr. Eng."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"6703","DOI":"10.1109\/TVT.2015.2480244","article-title":"Host-Based Intrusion Detection for VANETs: A Statistical Approach to Rogue Node Detection","volume":"65","author":"Zaidi","year":"2016","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1080\/21642583.2018.1440260","article-title":"Intelligent intrusion detection in external communication systems for autonomous vehicles","volume":"6","author":"Alheeti","year":"2018","journal-title":"Syst. Sci. Control Eng."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"9960","DOI":"10.1109\/JIOT.2021.3119055","article-title":"A Novel Intrusion Detection Method Based on Lightweight Neural Network for Internet of Things","volume":"9","author":"Zhao","year":"2022","journal-title":"IEEE Internet Things J."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Yang, L., Moubayed, A., Hamieh, I., and Shami, A. (2019, January 9\u201313). Tree-based intelligent intrusion detection system in internet of vehicles. Proceedings of the 2019 IEEE Global Communications Conference, GLOBECOM 2019\u2014Proceedings, Waikoloa, HI, USA.","DOI":"10.1109\/GLOBECOM38437.2019.9013892"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Chen, Z., Simsek, M., Kantarci, B., and Djukic, P. (2021, January 7\u201311). All Predict Wisest Decides: A Novel Ensemble Method to Detect Intrusive Traffic in IoT Networks. Proceedings of the 2021 IEEE Global Communications Conference, GLOBECOM 2021\u2014Proceedings, Madrid, Spain.","DOI":"10.1109\/GLOBECOM46510.2021.9685318"},{"key":"ref_41","first-page":"157","article-title":"Intrusion Detection based on a Novel Hybrid Learning Approach","volume":"6","author":"Khalvati","year":"2018","journal-title":"J. AI Data Min."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Canbay, Y., and Sagiroglu, S. (2016, January 9\u201311). A hybrid method for intrusion detection. Proceedings of the 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015, Miami, FL, USA.","DOI":"10.1109\/ICMLA.2015.197"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"12569","DOI":"10.1109\/JIOT.2020.3029248","article-title":"DaaS: Dew Computing as a Service for Intelligent Intrusion Detection in Edge-of-Things Ecosystem","volume":"8","author":"Singh","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_44","unstructured":"Albers, P., Camp, O., Percher, J., Jouga, B., M\u00e9, L., and Puttini, R. (2002, January 2\u20133). Security in Ad Hoc Networks: A General Intrusion Detection Architecture Enhancing Trust Based Approaches. Proceedings of the Wireless Information Systems, 1st International Workshop on Wireless Information Systems, WIS 2002 in Conjunction with ICEIS 2002, Ciudad Real, Spain."},{"key":"ref_45","unstructured":"Sterne, D., Balasubramanyam, P., Carman, D., Wilson, B., Talpade, R., Ko, C., Balupari, R., Tseng, C.-Y., Bowen, T., and Levitt, K. (2005, January 23\u201324). A general cooperative intrusion detection architecture for MANETs. Proceedings of the 3rd IEEE International Workshop on Information Assurance, IWIA 2005, College Park, MD, USA."},{"key":"ref_46","first-page":"155","article-title":"Machine learning applied to cyber operations","volume":"55","author":"Blowers","year":"2014","journal-title":"Adv. Inf. Secur."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1016\/j.eswa.2010.06.066","article-title":"A novel intrusion detection system based on hierarchical clustering and support vector machines","volume":"38","author":"Horng","year":"2011","journal-title":"Expert Syst. Appl."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Muda, Z., Yassin, W., Sulaiman, M.N., and Udzir, N.I. (2011, January 12\u201313). Intrusion detection based on K-Means clustering and Na\u00efve Bayes classification. Proceedings of the 2011 7th International Conference on Information Technology in Asia, Sarawak, Malaysia.","DOI":"10.1109\/ISIAS.2011.6122818"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Ghalwash, A., El Khameesy, N., Magdi, D., and Joshi, A. (2020). Enhancing IoT Botnets Attack Detection Using Machine Learning-IDS and Ensemble Data Preprocessing Technique. Internet of Things\u2014Applications and Future, Springer. Lecture Notes in Networks and Systems.","DOI":"10.1007\/978-981-15-3075-3"},{"key":"ref_50","first-page":"660","article-title":"Comparative study between metaheuristic algorithms for internet of things wireless nodes localization","volume":"12","author":"Mohammed","year":"2022","journal-title":"Int. J. Electr. Comput. Eng."},{"key":"ref_51","first-page":"2021","article-title":"Detection Technique and Mitigation Against a Phishing Attack","volume":"12","author":"Aboelfetouh","year":"2021","journal-title":"Int. J. Adv. Comput. Sci. Appl. (IJACSA)"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1016\/j.gsf.2020.03.007","article-title":"Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization","volume":"12","author":"Zhang","year":"2020","journal-title":"Geosci. Front."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.gr.2022.10.004","article-title":"Efficient time-variant reliability analysis of Bazimen landslide in the Three Gorges Reservoir Area using XGBoost and LightGBM algorithms","volume":"123","author":"Zhang","year":"2023","journal-title":"Gondwana Res."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/15\/10\/583\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:01:15Z","timestamp":1760112075000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/15\/10\/583"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,24]]},"references-count":53,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2024,10]]}},"alternative-id":["info15100583"],"URL":"https:\/\/doi.org\/10.3390\/info15100583","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,24]]}}}