{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T13:03:24Z","timestamp":1765803804490,"version":"3.48.0"},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"3","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:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T00:00:00Z","timestamp":1753833600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. ITS Res."],"published-print":{"date-parts":[[2025,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Connected and Autonomous Vehicle (CAV) technology is rising in the transport sector due to its enormous benefits, including better traffic safety and efficiency. The intelligent decision in CAV relies on traffic data from in-vehicle sensors, neighbouring vehicles, and roadside infrastructure. The traffic data is prone to manipulation by internal and external attacks. This NTU\u2019s CAV research focuses on message manipulation attacks, such as false position attacks. In this context, this paper proposes a novel misbehaviour prediction framework to identify malicious positions in CAV networks based on an aggregated trust analysis strategy. The proposed framework utilises Mamdani fuzzy logic for identification and will be applied to the onboard unit of every vehicle without depending on external infrastructure for validation. Through repeated observation of the concerned CAVs\u2019 behaviour and trust values aggregation, the framework can predict potential misbehaviour accurately before it leads to safety-critical issues. The framework is validated using the\n                    <jats:italic>VeReMi<\/jats:italic>\n                    extension dataset and tested with supervised machine learning algorithms, including Decision Tree (DT), k-nearest Neighbour (KNN), Logistic Regression (LR), and Support Vector Machine (SVM). The simulation results show that the DT and SVM yield the highest sensitivity in detecting false positions. Although we used the results with KNN, the default module in previous studies, we discovered that it was competitively effective in identifying all position attacks, doing very well with eventual stop position attacks. Additional examination of the trust aggregation fuzzy model indicates that the F1 measure for constant false position attacks, random position attacks, and eventual stop position attacks is above 0.99 on average. In contrast, the F1 score for random and fixed offsets is less than 0.70. To achieve improved performance, we use weights that are inversely proportional to the sender-receiver distance for post-processing. We also analyse the performance of our proposed model against other fuzzy logic models. Overall, our fuzzy model shows competitive performance in detecting false position data, as indicated by the performance values.\n                  <\/jats:p>","DOI":"10.1007\/s13177-025-00528-2","type":"journal-article","created":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T11:36:25Z","timestamp":1753875385000},"page":"1554-1570","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Misbehaviour Prediction in CAV Network using Aggregated Trust Analysis"],"prefix":"10.1007","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7996-9831","authenticated-orcid":false,"given":"Maria Drolence","family":"Mwanje","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9669-8244","authenticated-orcid":false,"given":"Omprakash","family":"Kaiwartya","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1322-2588","authenticated-orcid":false,"given":"Devki Nandan","family":"Jha","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1850-3269","authenticated-orcid":false,"given":"Ahmad M.","family":"Khasawneh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5746-0257","authenticated-orcid":false,"given":"Ali","family":"Sadiq","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2098-7637","authenticated-orcid":false,"given":"Yue","family":"Cao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,7,30]]},"reference":[{"issue":"2","key":"528_CR1","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1109\/TITS.2020.3016307","volume":"23","author":"SMO Gani","year":"2022","unstructured":"Gani, S.M.O., Fallah, Y.P., Krishnan, H.: Robust and scalable v2v safety communication based on the sae j2945\/1 standard. IEEE Trans. Intell. Transp. Syst. 23(2), 861\u2013872 (2022)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"doi-asserted-by":"crossref","unstructured":"Leinm\u00fcller, T.,\u00a0Schoch, E.,\u00a0Kargl, F.,\u00a0Maih\u00f6fer, C.: Influence of falsified position data on geographic ad-hoc routing. In: Security and Privacy in Ad-hoc and Sensor Networks: Second European Workshop, ESAS 2005, Visegrad, Hungary, July 13-14, 2005. Revised Selected Papers 2, pp.\u00a0102\u2013112, Springer (2005)","key":"528_CR2","DOI":"10.1007\/11601494_9"},{"unstructured":"Leinm\u00fcller, T.,\u00a0Schoch, E.: Greedy routing in highway scenarios: The impact of position faking nodes. In: Proceedings of Workshop On Intelligent Transportation (WIT 2006)(Mar. 2006) (2006)","key":"528_CR3"},{"doi-asserted-by":"crossref","unstructured":"Grover, J., Gaur, M.S.,\u00a0Laxmi, V.: Position forging attacks in vehicular ad hoc networks: implementation, impact and detection. In: 2011 7th International Wireless Communications and Mobile Computing Conference, pp.\u00a0701\u2013706, IEEE (2011)","key":"528_CR4","DOI":"10.1109\/IWCMC.2011.5982632"},{"doi-asserted-by":"crossref","unstructured":"Gyawali, S.,\u00a0Qian, Y.: Misbehavior detection using machine learning in vehicular communication networks. In: ICC 2019-2019 IEEE International Conference on Communications (ICC), pp.\u00a01\u20136, IEEE (2019)","key":"528_CR5","DOI":"10.1109\/ICC.2019.8761300"},{"doi-asserted-by":"crossref","unstructured":"So, S.,\u00a0Sharma, P.,\u00a0Petit, J.: Integrating plausibility checks and machine learning for misbehavior detection in vanet. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), pp.\u00a0564\u2013571, IEEE (2018)","key":"528_CR6","DOI":"10.1109\/ICMLA.2018.00091"},{"doi-asserted-by":"crossref","unstructured":"Singh, P.K.,\u00a0Gupta, S.,\u00a0Vashistha, R., Nandi, S.K.,\u00a0Nandi, S.: Machine learning based approach to detect position falsification attack in vanets. In:\u00a0Nandi, S.,\u00a0Jinwala, D.,\u00a0Singh, V.,\u00a0Laxmi, V., Gaur, M.S.,\u00a0Faruki, P. (eds.) Security and Privacy, (Singapore), pp.\u00a0166\u2013178, Springer Singapore (2019)","key":"528_CR7","DOI":"10.1007\/978-981-13-7561-3_13"},{"doi-asserted-by":"crossref","unstructured":"Mahmoudi, I.,\u00a0Kamel, J.,\u00a0Ben-Jemaa, I.,\u00a0Kaiser, A.,\u00a0Urien, P.: Towards a reliable machine learning-based global misbehavior detection in c\u2013its: Model evaluation approach. In: Vehicular Ad-hoc Networks for Smart Cities: Third International Workshop, 2019, pp.\u00a073\u201386, Springer (2020)","key":"528_CR8","DOI":"10.1007\/978-981-15-3750-9_6"},{"doi-asserted-by":"crossref","unstructured":"Sharma, A.,\u00a0Jaekel, A.: Machine learning approach for detecting location spoofing in vanet. In: 2021 International Conference on Computer Communications and Networks (ICCCN), pp.\u00a01\u20136 (2021)","key":"528_CR9","DOI":"10.1109\/ICCCN52240.2021.9522170"},{"doi-asserted-by":"crossref","unstructured":"Ercan, S.,\u00a0Ayaida, M.,\u00a0Messai, N.: New features for position falsification detection in vanets using machine learning. In: ICC 2021-IEEE International conference on communications, pp.\u00a01\u20136, IEEE (2021)","key":"528_CR10","DOI":"10.1109\/ICC42927.2021.9500411"},{"issue":"1","key":"528_CR11","doi-asserted-by":"publisher","first-page":"26","DOI":"10.36244\/ICJ.2023.1.4","volume":"15","author":"N Bereczki","year":"2023","unstructured":"Bereczki, N., Simon, V.: Machine learning use-cases in c-its applications. Infocommun. J. 15(1), 26\u201343 (2023)","journal-title":"Infocommun. J."},{"issue":"1","key":"528_CR12","doi-asserted-by":"publisher","first-page":"381","DOI":"10.21275\/ART20203995","volume":"9","author":"B Mahesh","year":"2020","unstructured":"Mahesh, B.: Machine learning algorithms-a review. Int. J. Sci. Res. (IJSR). [Internet] 9(1), 381\u2013386 (2020)","journal-title":"Int. J. Sci. Res. (IJSR). [Internet]"},{"doi-asserted-by":"crossref","unstructured":"Shaikh, R.A., Alzahrani, A.S.: Intrusion-aware trust model for vehicular ad hoc networks. Sec. Commun. Netw. 7(11), 1652\u20131669 (2014)","key":"528_CR13","DOI":"10.1002\/sec.862"},{"doi-asserted-by":"crossref","unstructured":"Wu, A.,\u00a0Ma, J.,\u00a0Zhang, S.: Rate: A rsu-aided scheme for data-centric trust establishment in vanets. In: 2011 7th International Conference on Wireless Communications, Networking and Mobile Computing, pp.\u00a01\u20136 (2011)","key":"528_CR14","DOI":"10.1109\/wicom.2011.6040302"},{"doi-asserted-by":"crossref","unstructured":"Raya, M.,\u00a0Papadimitratos, P., Gligor, V.D., Hubaux, J.-P.: On data-centric trust establishment in ephemeral ad hoc networks. In: IEEE INFOCOM 2008-The 27th Conference on Computer Communications, pp.\u00a01238\u20131246, IEEE (2008)","key":"528_CR15","DOI":"10.1109\/INFOCOM.2008.180"},{"doi-asserted-by":"crossref","unstructured":"Li, X.,\u00a0Liu, J.,\u00a0Li, X.,\u00a0Sun, W.: Rgte: A reputation-based global trust establishment in vanets. In: 2013 5th International Conference on Intelligent Networking and Collaborative Systems, pp.\u00a0210\u2013214, IEEE (2013)","key":"528_CR16","DOI":"10.1109\/INCoS.2013.91"},{"issue":"4","key":"528_CR17","doi-asserted-by":"publisher","first-page":"960","DOI":"10.1109\/TITS.2015.2494017","volume":"17","author":"W Li","year":"2015","unstructured":"Li, W., Song, H.: Art: An attack-resistant trust management scheme for securing vehicular ad hoc networks. IEEE Trans. Intell. Transp. Syst. 17(4), 960\u2013969 (2015)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"doi-asserted-by":"crossref","unstructured":"Patra, M.,\u00a0Acharya, S.: A fuzzy logic-based trust management scheme for wireless sensor network. In: Advances in Distributed Computing and Machine Learning: Proceedings of ICADCML 2022, pp.\u00a0155\u2013166, Springer (2022)","key":"528_CR18","DOI":"10.1007\/978-981-19-1018-0_14"},{"key":"528_CR19","doi-asserted-by":"publisher","first-page":"15619","DOI":"10.1109\/ACCESS.2017.2733225","volume":"5","author":"SA Soleymani","year":"2017","unstructured":"Soleymani, S.A., Abdullah, A.H., Zareei, M., Anisi, M.H., Vargas-Rosales, C., Khan, M.K., Goudarzi, S.: A secure trust model based on fuzzy logic in vehicular ad hoc networks with fog computing. IEEE Access 5, 15619\u201315629 (2017)","journal-title":"IEEE Access"},{"issue":"12","key":"528_CR20","doi-asserted-by":"publisher","first-page":"14037","DOI":"10.1109\/TITS.2023.3305342","volume":"24","author":"MM Hasan","year":"2023","unstructured":"Hasan, M.M., Jahan, M., Kabir, S.: A trust model for edge-driven vehicular ad hoc networks using fuzzy logic. IEEE Trans. Intell. Transp. Syst. 24(12), 14037\u201314050 (2023)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"1","key":"528_CR21","first-page":"7608198","volume":"2018","author":"H Xia","year":"2018","unstructured":"Xia, H., Zhang, S.-S., Li, B.-X., Li, L., Cheng, X.-G.: Towards a novel trust-based multicast routing for vanets. Sec. Commun. Netw. 2018(1), 7608198 (2018)","journal-title":"Sec. Commun. Netw."},{"issue":"2","key":"528_CR22","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1109\/91.493904","volume":"4","author":"LA Zadeh","year":"1996","unstructured":"Zadeh, L.A.: Fuzzy logic= computing with words. IEEE Trans. Fuzzy Syst. 4(2), 103\u2013111 (1996)","journal-title":"IEEE Trans. Fuzzy Syst."},{"unstructured":"Palit, A.K.,\u00a0Popovic, D.: Computational intelligence in time series forecasting: theory and engineering applications. Springer Science & Business Media (2006)","key":"528_CR23"},{"doi-asserted-by":"crossref","unstructured":"Alonso, J.M.,\u00a0Magdalena, L.: Special issue on interpretable fuzzy systems (2011)","key":"528_CR24","DOI":"10.1016\/j.ins.2011.07.001"},{"doi-asserted-by":"crossref","unstructured":"Kadam, N., Krovi, R.S.: Machine learning approach of hybrid ksvn algorithm to detect ddos attack in vanet. Int. J. Adv. Comp. Sci. Appl. 12(7), (2021)","key":"528_CR25","DOI":"10.14569\/IJACSA.2021.0120782"},{"issue":"4","key":"528_CR26","doi-asserted-by":"publisher","first-page":"3613","DOI":"10.1007\/s11277-020-07549-y","volume":"114","author":"K Adhikary","year":"2020","unstructured":"Adhikary, K., Bhushan, S., Kumar, S., Dutta, K.: Hybrid algorithm to detect ddos attacks in vanets. Wireless Pers. Commun. 114(4), 3613\u20133634 (2020)","journal-title":"Wireless Pers. Commun."},{"key":"528_CR27","first-page":"100037","volume":"2","author":"H Setia","year":"2024","unstructured":"Setia, H., Chhabra, A., Singh, S.K., Kumar, S., Sharma, S., Arya, V., Gupta, B.B., Wu, J.: Securing the road ahead: Machine learning-driven ddos attack detection in vanet cloud environments. Cyber Sec. Appl. 2, 100037 (2024)","journal-title":"Cyber Sec. Appl."},{"doi-asserted-by":"crossref","unstructured":"Rahal, R., Amara Korba, A., Ghoualmi-Zine, N.: Towards the development of realistic dos dataset for intelligent transportation systems. Wireless Person. Commun. 115(2), 1415\u20131444 (2020)","key":"528_CR28","DOI":"10.1007\/s11277-020-07635-1"},{"unstructured":"Ali, S.,\u00a0Nand, P.,\u00a0Tiwari, S.: Detection of wormhole attack in vehicular ad-hoc network over real map using machine learning approach with preventive scheme. J. Inf. Technol. Manag 14, pp.\u00a0159\u2013179, no.\u00a0Special Issue: Security and Resource Management challenges for Internet of Things (2022)","key":"528_CR29"},{"doi-asserted-by":"crossref","unstructured":"Prasad, M.,\u00a0Tripathi, S.,\u00a0Dahal, K.: Wormhole attack detection in ad hoc network using machine learning technique. In: 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp.\u00a01\u20137 (2019)","key":"528_CR30","DOI":"10.1109\/ICCCNT45670.2019.8944634"},{"doi-asserted-by":"crossref","unstructured":"Gu, P.,\u00a0Khatoun, R.,\u00a0Begriche, Y.,\u00a0Serhrouchni, A.: Support vector machine (svm) based sybil attack detection in vehicular networks. In: 2017 IEEE Wireless Communications and Networking Conference (WCNC), pp.\u00a01\u20136 (2017)","key":"528_CR31","DOI":"10.1109\/WCNC.2017.7925783"},{"issue":"8","key":"528_CR32","doi-asserted-by":"publisher","first-page":"3435","DOI":"10.1007\/s11276-023-03399-1","volume":"29","author":"A Balaram","year":"2023","unstructured":"Balaram, A., Nabi, S.A., Rao, K.S., Koppula, N.: Highly accurate sybil attack detection in vanet using extreme learning machine with preserved location. Wireless Netw. 29(8), 3435\u20133443 (2023)","journal-title":"Wireless Netw."},{"issue":"18","key":"528_CR33","doi-asserted-by":"publisher","first-page":"6934","DOI":"10.3390\/s22186934","volume":"22","author":"S Azam","year":"2022","unstructured":"Azam, S., Bibi, M., Riaz, R., Rizvi, S.S., Kwon, S.J.: Collaborative learning based sybil attack detection in vehicular ad-hoc networks (vanets). Sensors 22(18), 6934 (2022)","journal-title":"Sensors"},{"doi-asserted-by":"crossref","unstructured":"Kumar, A., Shahid, M.A.,\u00a0Jaekel, A.,\u00a0Zhang, N.,\u00a0Kneppers, M.: Machine learning based detection of replay attacks in vanet. In: NOMS 2023-2023 IEEE\/IFIP Network Operations and Management Symposium, pp.\u00a01\u20136 (2023)","key":"528_CR34","DOI":"10.1109\/NOMS56928.2023.10154299"},{"doi-asserted-by":"crossref","unstructured":"Sedar, R.,\u00a0Kalalas, C.,\u00a0V\u00e1zquez-Gallego, F.,\u00a0Alonso-Zarate, J.: Reinforcement learning based misbehavior detection in vehicular networks. In: ICC 2022 - IEEE International Conference on Communications, pp.\u00a03550\u20133555 (2022)","key":"528_CR35","DOI":"10.1109\/ICC45855.2022.9838796"},{"key":"528_CR36","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1016\/j.vehcom.2018.05.001","volume":"13","author":"D Karagiannis","year":"2018","unstructured":"Karagiannis, D., Argyriou, A.: Jamming attack detection in a pair of rf communicating vehicles using unsupervised machine learning. Vehicular Commun. 13, 56\u201363 (2018)","journal-title":"Vehicular Commun."},{"doi-asserted-by":"crossref","unstructured":"Grover, J., Prajapati, N.K.,\u00a0Laxmi, V., Gaur, M.S.: Machine learning approach for multiple misbehavior detection in vanet. In: Advances in Computing and Communications: First International Conference, ACC 2011, Kochi, India, July 22-24, 2011, Proceedings, Part III 1, pp.\u00a0644\u2013653, Springer (2011)","key":"528_CR37","DOI":"10.1007\/978-3-642-22720-2_68"},{"issue":"1","key":"528_CR38","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1109\/COMST.2021.3129079","volume":"24","author":"A Talpur","year":"2022","unstructured":"Talpur, A., Gurusamy, M.: Machine learning for security in vehicular networks: A comprehensive survey. IEEE Commun. Surv. Tutor. 24(1), 346\u2013379 (2022)","journal-title":"IEEE Commun. Surv. Tutor."},{"issue":"5","key":"528_CR39","doi-asserted-by":"publisher","first-page":"2153","DOI":"10.1007\/s12083-023-01508-7","volume":"16","author":"J Nagarajan","year":"2023","unstructured":"Nagarajan, J., Mansourian, P., Shahid, M.A., Jaekel, A., Saini, I., Zhang, N., Kneppers, M.: Machine learning based intrusion detection systems for connected autonomous vehicles: A survey. Peer-to-Peer Netw. Appl. 16(5), 2153\u20132185 (2023)","journal-title":"Peer-to-Peer Netw. Appl."},{"issue":"11","key":"528_CR40","first-page":"1024","volume":"44","author":"R Sultana","year":"2022","unstructured":"Sultana, R., Grover, J., Meghwal, J., Tripathi, M.: Exploiting machine learning and deep learning models for misbehavior detection in vanet. Int. J. Comput. Appl. 44(11), 1024\u20131038 (2022)","journal-title":"Int. J. Comput. Appl."},{"doi-asserted-by":"crossref","unstructured":"Kamel, J.,\u00a0Wolf, M., Van Der\u00a0Hei, R.W.,\u00a0Kaiser, A.,\u00a0Urien, P.,\u00a0Kargl, F.: Veremi extension: A dataset for comparable evaluation of misbehavior detection in vanets. In: ICC 2020-2020 IEEE International Conference on Communications (ICC), pp.\u00a01\u20136, IEEE (2020)","key":"528_CR41","DOI":"10.1109\/ICC40277.2020.9149132"},{"doi-asserted-by":"crossref","unstructured":"Kamel, J.,\u00a0Kaiser, A.,\u00a0ben Jemaa, I.,\u00a0Cincilla, P.,\u00a0Urien, P.: Catch: a confidence range tolerant misbehavior detection approach. In: 2019 IEEE Wireless Communications and Networking Conference (WCNC), pp.\u00a01\u20138, IEEE (2019)","key":"528_CR42","DOI":"10.1109\/WCNC.2019.8885740"},{"doi-asserted-by":"crossref","unstructured":"So, S.,\u00a0Petit, J.,\u00a0Starobinski, D.: Physical layer plausibility checks for misbehavior detection in v2x networks. In: Proceedings of the 12th conference on security and privacy in wireless and mobile networks, pp.\u00a084\u201393 (2019)","key":"528_CR43","DOI":"10.1145\/3317549.3323406"},{"doi-asserted-by":"crossref","unstructured":"Sharma, P.,\u00a0Austin, D.,\u00a0Liu, H.: Attacks on machine learning: Adversarial examples in connected and autonomous vehicles. In: 2019 IEEE International Symposium on Technologies for Homeland Security (HST), pp.\u00a01\u20137, IEEE (2019)","key":"528_CR44","DOI":"10.1109\/HST47167.2019.9032989"},{"doi-asserted-by":"crossref","unstructured":"Sultana, R.,\u00a0Grover, J.,\u00a0Tripathi, M.: A data-centric and dynamic-range based misbehavior detection approach for vanet. In: TENCON 2022 - 2022 IEEE Region 10 Conference (TENCON), pp.\u00a01\u20136 (2022)","key":"528_CR45","DOI":"10.1109\/TENCON55691.2022.9977588"},{"doi-asserted-by":"crossref","unstructured":"Suthaharan, S.,\u00a0Suthaharan, S.: Support vector machine. Mach. Learn. Models Alg. Big Data Classif.: Think Examp. Effect. Learn. 207\u2013235 (2016)","key":"528_CR46","DOI":"10.1007\/978-1-4899-7641-3_9"},{"doi-asserted-by":"crossref","unstructured":"Mucherino, A., Papajorgji, P.J., Pardalos, P.M.,\u00a0Mucherino, A., Papajorgji, P.J., Pardalos, P.M.: K-nearest neighbor classification. Data Min. Agricult. 83\u2013106 (2009)","key":"528_CR47","DOI":"10.1007\/978-0-387-88615-2_4"},{"issue":"4","key":"528_CR48","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1109\/TSMC.1976.5408784","volume":"SMC\u20136","author":"SA Dudani","year":"1976","unstructured":"Dudani, S.A.: The distance-weighted k-nearest-neighbor rule. IEEE Trans. Syst. Man Cybern. SMC\u20136(4), 325\u2013327 (1976)","journal-title":"IEEE Trans. Syst. Man Cybern."},{"issue":"2","key":"528_CR49","first-page":"334","volume":"3","author":"A Priyam","year":"2013","unstructured":"Priyam, A., Abhijeeta, G.R., Rathee, A., Srivastava, S.: Comparative analysis of decision tree classification algorithms. Intern. J. Current Eng. Technol. 3(2), 334\u2013337 (2013)","journal-title":"Intern. J. Current Eng. Technol."},{"issue":"9","key":"528_CR50","doi-asserted-by":"publisher","first-page":"1011","DOI":"10.1038\/nbt0908-1011","volume":"26","author":"C Kingsford","year":"2008","unstructured":"Kingsford, C., Salzberg, S.L.: What are decision trees? Nat. Biotechnol. 26(9), 1011\u20131013 (2008)","journal-title":"Nat. Biotechnol."},{"issue":"4","key":"528_CR51","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1109\/2.53","volume":"21","author":"LA Zadeh","year":"1988","unstructured":"Zadeh, L.A.: Fuzzy logic. Computer 21(4), 83\u201393 (1988)","journal-title":"Computer"},{"doi-asserted-by":"crossref","unstructured":"Drolence\u00a0Mwanje, M.,\u00a0Kaiwartya, O.,\u00a0Naser, A.: Position verification in connected vehicles for cyber resilience using geofencing and fuzzy logic. IEEE Open J. Intell. Transp. Syst. 5, 540\u2013554 (2024)","key":"528_CR52","DOI":"10.1109\/OJITS.2024.3453666"},{"doi-asserted-by":"crossref","unstructured":"Hastie, T.,\u00a0Tibshirani, R., Friedman, J.H., Friedman, J.H.: The elements of statistical learning: data mining, inference, and prediction, vol 2. Springer (2009)","key":"528_CR53","DOI":"10.1007\/978-0-387-84858-7"}],"container-title":["International Journal of Intelligent Transportation Systems Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13177-025-00528-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13177-025-00528-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13177-025-00528-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T12:58:02Z","timestamp":1765803482000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13177-025-00528-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,30]]},"references-count":53,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["528"],"URL":"https:\/\/doi.org\/10.1007\/s13177-025-00528-2","relation":{},"ISSN":["1348-8503","1868-8659"],"issn-type":[{"type":"print","value":"1348-8503"},{"type":"electronic","value":"1868-8659"}],"subject":[],"published":{"date-parts":[[2025,7,30]]},"assertion":[{"value":"2 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 June 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 July 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 July 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"N\/A. This research does not require any ethics approval or consent to participate.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval and Consent to Participate"}},{"value":"This manuscript does not contain any personal data related to individuals in any form. Therefore, it is not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}},{"value":"The authors declare no conflict of interest regarding the research and publication of this research.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}]}}