{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T16:40:33Z","timestamp":1743093633156,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":27,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789811941085"},{"type":"electronic","value":"9789811941092"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-981-19-4109-2_6","type":"book-chapter","created":{"date-parts":[[2022,8,1]],"date-time":"2022-08-01T14:38:26Z","timestamp":1659364706000},"page":"52-61","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Many-Objective Anomaly Detection Model for Vehicle Network Based on Federated Learning and Differential Privacy Protection"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3995-7112","authenticated-orcid":false,"given":"Tian","family":"Fan","sequence":"first","affiliation":[]},{"given":"Zhixia","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Yang","family":"Lan","sequence":"additional","affiliation":[]},{"given":"Zhihua","family":"Cui","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,8,1]]},"reference":[{"issue":"2","key":"6_CR1","doi-asserted-by":"publisher","first-page":"1484","DOI":"10.1109\/TVT.2019.2961344","volume":"69","author":"H Olufowobi","year":"2020","unstructured":"Olufowobi, H., Young, C., Zambreno, J., Bloom, G.: Specification-based automotive intrusion detection using controller area network (CAN) timing. IEEE Trans. Veh. Technol. 69(2), 1484\u20131494 (2020)","journal-title":"IEEE Trans. Veh. Technol."},{"issue":"7","key":"6_CR2","doi-asserted-by":"publisher","first-page":"3939","DOI":"10.1109\/TITS.2020.2998775","volume":"22","author":"F Wei","year":"2021","unstructured":"Wei, F., Zeadally, S., Vijayakumar, P., Kumar, N., He, D.: An intelligent terminal based privacy-preserving multi-modal implicit authentication protocol for internet of connected vehicles. IEEE Trans. Intell. Transp. Syst. 22(7), 3939\u20133951 (2021)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"6_CR3","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1016\/j.comcom.2020.01.058","volume":"153","author":"D Sirohi","year":"2020","unstructured":"Sirohi, D., Kumar, N., Rana, P.: Convolutional neural networks for 5G-enabled intelligent transportation system: a systematic review. Comput. Commun. 153, 459\u2013498 (2020)","journal-title":"Comput. Commun."},{"key":"6_CR4","doi-asserted-by":"crossref","unstructured":"Javed, A.R., Rehman, S.U., Khan, M.U., Alazab, M., Thippa, R.: CANintelliIDS: detecting in-vehicle intrusion attacks on a controller area network using CNN and attention-based GRU. IEEE Trans. Netw. Sci. Eng. 8(2), 1456\u20131466 (2021)","DOI":"10.1109\/TNSE.2021.3059881"},{"issue":"5","key":"6_CR5","doi-asserted-by":"publisher","first-page":"4325","DOI":"10.1109\/TVT.2018.2795384","volume":"67","author":"P Murvay","year":"2018","unstructured":"Murvay, P., Groza, B.: Security shortcomings and countermeasures for the SAE J1939 commercial vehicle bus protocol. IEEE Trans. Veh. Technol. 67(5), 4325\u20134339 (2018)","journal-title":"IEEE Trans. Veh. Technol."},{"key":"6_CR6","doi-asserted-by":"crossref","unstructured":"M\u00fcter, M., Asaj, N.: Entropy-based anomaly detection for in-vehicle networks. In: 2011 IEEE Intelligent Vehicles Symposium (IV), pp. 1110\u20131115 (2011)","DOI":"10.1109\/IVS.2011.5940552"},{"key":"6_CR7","doi-asserted-by":"crossref","unstructured":"Kang, J., Tang, T.: Intrusion detection system using deep neural network for in-vehicle net-work security, Plos One, 11(6), e0155781 (2016)","DOI":"10.1371\/journal.pone.0155781"},{"issue":"4","key":"6_CR8","doi-asserted-by":"publisher","first-page":"79","DOI":"10.4236\/wet.2018.94007","volume":"9","author":"A Alshammari","year":"2018","unstructured":"Alshammari, A., Zohdy, M., Debnath, D., et al.: Classification approach for intrusion detection in vehicle systems. Wirel. Eng. Technol. 9(4), 79\u201394 (2018)","journal-title":"Wirel. Eng. Technol."},{"key":"6_CR9","doi-asserted-by":"crossref","unstructured":"Sargolzaei, A., Crane, C., Abbaspour, A., Noei, S.: A machine learning approach for fault detection in vehicular cyber-physical systems. In: 15th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 636\u2013640. IEEE, Anaheim (2016)","DOI":"10.1109\/ICMLA.2016.0112"},{"key":"6_CR10","doi-asserted-by":"crossref","unstructured":"Taylor, A., Leblanc, S., Japkowicz, N.: Anomaly detection in automobile control network data with long short-term memory networks. In: 3th IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp. 130\u2013139. IEEE, Montreal (2016)","DOI":"10.1109\/DSAA.2016.20"},{"issue":"6","key":"6_CR11","doi-asserted-by":"publisher","first-page":"5234","DOI":"10.1109\/TVT.2021.3057074","volume":"70","author":"Z Zhang","year":"2021","unstructured":"Zhang, Z., Cao, Y., Cui, Z., Zhang, W., Chen, J.: A Many-objective optimization based intelligent intrusion detection algorithm for enhancing security of vehicular networks in 6G. IEEE Trans. Veh. Technol. 70(6), 5234\u20135243 (2021)","journal-title":"IEEE Trans. Veh. Technol."},{"issue":"2","key":"6_CR12","first-page":"263","volume":"13","author":"M Hassan","year":"2020","unstructured":"Hassan, M., Rehmani, M., Chen, J.: Deal: Differentially private auction for blockchain-based microgrids energy trading. IEEE Trans. Serv. Comput. 13(2), 263\u2013275 (2020)","journal-title":"IEEE Trans. Serv. Comput."},{"issue":"19","key":"6_CR13","first-page":"11","volume":"234","author":"W Liu","year":"2017","unstructured":"Liu, W., Wang, Z., Liu, X., Zeng, N.: A survey of deep neural network architectures and their applications. Neurocomputing 234(19), 11\u201326 (2017)","journal-title":"Neurocomputing"},{"issue":"9","key":"6_CR14","first-page":"2604","volume":"27","author":"Q Liu","year":"2021","unstructured":"Liu, Q., Xu, X., Zhang, X., Dou, W.: Federated learning based method for intelligent computing with privacy preserving in edge computing. Comput. Integr. Manufact. Syst. 27(9), 2604\u20132610 (2021)","journal-title":"Comput. Integr. Manufact. Syst."},{"key":"6_CR15","doi-asserted-by":"crossref","unstructured":"Mansoor, A., Hadis, K., Muhammad, T.: Integration of blockchain and federated learning for Internet of Things: recent advances and future challenges. Comput. Secur. 108, 102355 (2021)","DOI":"10.1016\/j.cose.2021.102355"},{"issue":"2","key":"6_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3298981","volume":"10","author":"Q Yang","year":"2019","unstructured":"Yang, Q., Liu, Y., Chen, T., et al.: Federated machine learning. ACM Trans. Intell. Syst. Technol. 10(2), 1\u201319 (2019)","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"6_CR17","doi-asserted-by":"crossref","unstructured":"Shokri, R., Shmatikov, V.: Privacy preserving deep learning. In: 22th ACM SIGSAC Conference on Computer and Communications Security, pp. 1310\u20131321. ACM, New York (2015)","DOI":"10.1145\/2810103.2813687"},{"key":"6_CR18","doi-asserted-by":"crossref","unstructured":"Fan, T., Cui, Z.: Adaptive differential privacy preserving based on multi-objective optimization in deep neural networks. Concurr. Comput. Pract. Exper. 33(20), e6367 (2021)","DOI":"10.1002\/cpe.6367"},{"key":"6_CR19","doi-asserted-by":"crossref","unstructured":"Zou, J., Liu, J., Zheng, J., Yang, S.: A many-objective algorithm based on staged coordination selection. Swarm Evol. Comput. 60, 100737 (2021)","DOI":"10.1016\/j.swevo.2020.100737"},{"key":"6_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ins.2019.08.069","volume":"509","author":"H Zhao","year":"2020","unstructured":"Zhao, H., Zhang, C.: An online-learning-based evolutionary many-objective algorithm. Inf. Sci. 509, 1\u201321 (2020)","journal-title":"Inf. Sci."},{"issue":"4","key":"6_CR21","doi-asserted-by":"publisher","first-page":"3017","DOI":"10.1007\/s00366-020-00986-0","volume":"37","author":"G Dhiman","year":"2021","unstructured":"Dhiman, G., Soni, M., Pandey, H., Slowik, A., Kaur, H.: A novel hybrid hypervolume indicator and reference vector adaptation strategies based evolutionary algorithm for many-objective optimization. Eng. Comput. 37(4), 3017\u20133035 (2021)","journal-title":"Eng. Comput."},{"issue":"12","key":"6_CR22","doi-asserted-by":"publisher","first-page":"3665","DOI":"10.1109\/TFUZZ.2021.3089230","volume":"29","author":"X Cai","year":"2021","unstructured":"Cai, X., Zhang, J., et al.: A many-objective multistage optimization-based fuzzy decision-making model for coal production prediction. IEEE Trans. Fuzzy Syst. 29(12), 3665\u20133675 (2021)","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"6_CR23","doi-asserted-by":"publisher","first-page":"256","DOI":"10.1016\/j.ins.2020.01.018","volume":"158","author":"Z Cui","year":"2020","unstructured":"Cui, Z., Zhang, J., et al.: Hybrid many-objective particle swarm optimization algorithm for green coal production problem. Inf. Sci. 158, 256\u2013271 (2020)","journal-title":"Inf. Sci."},{"issue":"4","key":"6_CR24","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1109\/TEVC.2013.2281535","volume":"18","author":"K Deb","year":"2014","unstructured":"Deb, K., Jain, H.: An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: Solving problems with box constraints. IEEE Trans. Evol. Comput. 18(4), 577\u2013601 (2014)","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"5","key":"6_CR25","doi-asserted-by":"publisher","first-page":"721","DOI":"10.1109\/TEVC.2012.2227145","volume":"17","author":"S Yang","year":"2013","unstructured":"Yang, S., Li, M., Liu, X., Zheng, J.: A grid-based evolutionary algorithm for many-objective optimization. IEEE Trans. Evol. Comput. 17(5), 721\u2013736 (2013)","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"6","key":"6_CR26","doi-asserted-by":"publisher","first-page":"761","DOI":"10.1109\/TEVC.2014.2378512","volume":"19","author":"XY Zhang","year":"2015","unstructured":"Zhang, X.Y., Tian, Y., Jin, Y.C.: A knee point-driven evolutionary algorithm for many-objective optimization. IEEE Trans. Evol. Comput. 19(6), 761\u2013776 (2015)","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"1","key":"6_CR27","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1109\/TEVC.2016.2587808","volume":"21","author":"Y Xiang","year":"2017","unstructured":"Xiang, Y., Zhou, Y., Li, M., Chen, Z.: A vector angle-based evolutionary algorithm for unconstrained many-objective optimization. IEEE Trans. Evol. Comput. 21(1), 131\u2013152 (2017)","journal-title":"IEEE Trans. Evol. Comput."}],"container-title":["Communications in Computer and Information Science","Exploration of Novel Intelligent Optimization Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-19-4109-2_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,1]],"date-time":"2022-08-01T14:41:58Z","timestamp":1659364918000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-19-4109-2_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9789811941085","9789811941092"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-981-19-4109-2_6","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"1 August 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ISICA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Intelligence Computation and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Giangzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 November 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 November 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"isica2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/gdstinfo.scau.edu.cn\/isica2021\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Open","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"99","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"48","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"48% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}