{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T23:05:48Z","timestamp":1763161548123,"version":"3.45.0"},"publisher-location":"Singapore","reference-count":22,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819545742"},{"type":"electronic","value":"9789819545759"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-981-95-4575-9_11","type":"book-chapter","created":{"date-parts":[[2025,11,9]],"date-time":"2025-11-09T17:57:20Z","timestamp":1762711040000},"page":"162-176","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Explainable Artificial Intelligence for\u00a0Intrusion Detection in\u00a0Connected Vehicles"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-0988-8622","authenticated-orcid":false,"given":"Ramin","family":"Taheri","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6166-296X","authenticated-orcid":false,"given":"Alexander","family":"Gegov","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4336-2007","authenticated-orcid":false,"given":"Farzad","family":"Arabikhan","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0000-0610-8598","authenticated-orcid":false,"given":"Alexandar","family":"Ichtev","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6424-6590","authenticated-orcid":false,"given":"Petia","family":"Georgieva","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,11,10]]},"reference":[{"key":"11_CR1","doi-asserted-by":"publisher","first-page":"869","DOI":"10.1109\/OJVT.2024.3422253","volume":"5","author":"M Almehdhar","year":"2024","unstructured":"Almehdhar, M., et al.: Deep learning in the fast lane: a survey on advanced intrusion detection systems for intelligent vehicle networks. IEEE Open J. Veh. Technol. 5, 869\u2013906 (2024)","journal-title":"IEEE Open J. Veh. Technol."},{"issue":"6","key":"11_CR2","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1109\/MNET.2024.3390937","volume":"38","author":"Y Mei","year":"2024","unstructured":"Mei, Y., Han, W., Lin, K.: Intrusion detection for intelligent connected vehicles based on bidirectional temporal convolutional network. IEEE Netw. 38(6), 113\u2013119 (2024)","journal-title":"IEEE Netw."},{"issue":"2","key":"11_CR3","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1109\/IOTM.001.2300109","volume":"7","author":"N Quadar","year":"2024","unstructured":"Quadar, N., Chehri, A., Debaque, B., Ahmed, I., Jeon, G.: Intrusion detection systems in automotive ethernet networks: challenges, opportunities and future research trends. IEEE Internet Things Maga. 7(2), 62\u201368 (2024)","journal-title":"IEEE Internet Things Maga."},{"issue":"3","key":"11_CR4","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1109\/MNET.2024.3367303","volume":"38","author":"L Du","year":"2024","unstructured":"Du, L., Gu, Z., Wang, Y., Gao, C.: Open world intrusion detection: an open set recognition method for can bus in intelligent connected vehicles. IEEE Netw. 38(3), 76\u201382 (2024)","journal-title":"IEEE Netw."},{"issue":"1","key":"11_CR5","doi-asserted-by":"publisher","DOI":"10.1002\/widm.1515","volume":"14","author":"J Ahmad","year":"2024","unstructured":"Ahmad, J., et al.: Machine learning and blockchain technologies for cybersecurity in connected vehicles. Wiley interdisc. Rev. Data Min. Knowl. Disc. 14(1), e1515 (2024)","journal-title":"Wiley interdisc. Rev. Data Min. Knowl. Disc."},{"key":"11_CR6","doi-asserted-by":"publisher","first-page":"82402","DOI":"10.1109\/ACCESS.2024.3412933","volume":"12","author":"X Wang","year":"2024","unstructured":"Wang, X., Xu, Y., Xu, Y., Wang, Z., Wu, Y.: Intrusion detection system for in-vehicle can-fd bus id based on gan model. IEEE Access 12, 82402\u201382412 (2024)","journal-title":"IEEE Access"},{"issue":"9","key":"11_CR7","doi-asserted-by":"publisher","first-page":"12682","DOI":"10.1109\/TVT.2024.3383449","volume":"73","author":"J Qin","year":"2024","unstructured":"Qin, J., Xun, Y., Deng, Z., Liu, J.: GPIDS: GAN assisted contextual pattern-aware intrusion detection system for IVN. IEEE Trans. Veh. Technol. 73(9), 12682\u201312693 (2024)","journal-title":"IEEE Trans. Veh. Technol."},{"key":"11_CR8","doi-asserted-by":"publisher","first-page":"28911","DOI":"10.1109\/ACCESS.2024.3368392","volume":"12","author":"K Zhang","year":"2024","unstructured":"Zhang, K., et al.: Intrusion detection model for Internet of Vehicles using GRIPCA and OWELM. IEEE Access 12, 28911\u201328925 (2024)","journal-title":"IEEE Access"},{"issue":"2","key":"11_CR9","doi-asserted-by":"publisher","first-page":"1909","DOI":"10.1109\/TVT.2024.3402366","volume":"74","author":"M Fu","year":"2024","unstructured":"Fu, M., Wang, P., Liu, M., Zhang, Z., Zhou, X.: IoV-BERT-IDS: hybrid network intrusion detection system in IoV using large language models. IEEE Trans. Veh. Technol. 74(2), 1909\u20131921 (2024)","journal-title":"IEEE Trans. Veh. Technol."},{"issue":"3","key":"11_CR10","doi-asserted-by":"publisher","first-page":"2203","DOI":"10.1007\/s10207-024-00821-3","volume":"23","author":"D Micale","year":"2024","unstructured":"Micale, D., Matteucci, I., Fenzl, F., Rieke, R., Patan\u00e8, G.: A context-aware on-board intrusion detection system for smart vehicles. Int. J. Inf. Secur. 23(3), 2203\u20132223 (2024)","journal-title":"Int. J. Inf. Secur."},{"issue":"13","key":"11_CR11","doi-asserted-by":"publisher","first-page":"20908","DOI":"10.1109\/JSEN.2024.3397966","volume":"24","author":"M Moradi","year":"2024","unstructured":"Moradi, M., et al.: Sensor and decision fusion-based intrusion detection and mitigation approach for connected autonomous vehicles. IEEE Sens. J. 24(13), 20908\u201320919 (2024)","journal-title":"IEEE Sens. J."},{"issue":"1","key":"11_CR12","doi-asserted-by":"publisher","first-page":"12236","DOI":"10.1038\/s41598-025-94445-9","volume":"15","author":"H Khan","year":"2025","unstructured":"Khan, H., Tejani, G.G., AlGhamdi, R., Alasmari, S., Sharma, N.K., Sharma, S.K.: A secure and efficient deep learning-based intrusion detection framework for the internet of vehicles. Sci. Rep. 15(1), 12236 (2025)","journal-title":"Sci. Rep."},{"issue":"1","key":"11_CR13","doi-asserted-by":"publisher","first-page":"3867","DOI":"10.1109\/TCE.2024.3372691","volume":"70","author":"N Nissar","year":"2024","unstructured":"Nissar, N., Naja, N., Jamali, A.: Securing VANETs: multi-objective intrusion detection with variational autoencoders. IEEE Trans. Consum. Electron. 70(1), 3867\u20133874 (2024)","journal-title":"IEEE Trans. Consum. Electron."},{"key":"11_CR14","doi-asserted-by":"publisher","first-page":"23954","DOI":"10.1109\/ACCESS.2024.3365140","volume":"12","author":"O Arreche","year":"2024","unstructured":"Arreche, O., Guntur, T.R., Roberts, J.W., Abdallah, M.: E-xai: evaluating black-box explainable AI frameworks for network intrusion detection. IEEE Access 12, 23954\u201323988 (2024)","journal-title":"IEEE Access"},{"key":"11_CR15","doi-asserted-by":"crossref","unstructured":"Vishnukumar, R., Padma, A., Ramaiah, M.: Intelligent connected vehicle intrusion detection and mitigation: an analysis of explainable AI. In: 2024 International Conference on Emerging Techniques in Computational Intelligence (ICETCI), pp. 252\u2013256. IEEE (2024)","DOI":"10.1109\/ICETCI62771.2024.10704143"},{"key":"11_CR16","doi-asserted-by":"publisher","first-page":"48583","DOI":"10.1109\/ACCESS.2024.3383431","volume":"12","author":"S Nazat","year":"2024","unstructured":"Nazat, S., Li, L., Abdallah, M.: XAI-ADS: an explainable artificial intelligence framework for enhancing anomaly detection in autonomous driving systems. IEEE Access 12, 48583\u201348607 (2024)","journal-title":"IEEE Access"},{"key":"11_CR17","doi-asserted-by":"publisher","first-page":"7231","DOI":"10.1109\/TITS.2025.3525505","volume":"26","author":"M Wazid","year":"2025","unstructured":"Wazid, M., et al.: Explainable deep learning-enabled malware attack detection for iot-enabled intelligent transportation systems. IEEE Trans. Intell. Transport. Syst. 26, 7231\u20137244 (2025)","journal-title":"IEEE Trans. Intell. Transport. Syst."},{"key":"11_CR18","doi-asserted-by":"publisher","first-page":"2282","DOI":"10.1109\/TITS.2024.3509459","volume":"26","author":"W Aljabri","year":"2024","unstructured":"Aljabri, W., Hamid, M.A., Mosli, R.: Lightweight and adaptive data-driven intrusion detection system for autonomous vehicles. IEEE Trans. Intell. Transport. Syst. 26, 2282\u20132292 (2024)","journal-title":"IEEE Trans. Intell. Transport. Syst."},{"key":"11_CR19","unstructured":"Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. Adv. Neural Inf. Process. Syst. 30 (2017)"},{"key":"11_CR20","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., Guestrin, C.: \u201cwhy should i trust you?\u201d explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135\u20131144 (2016)","DOI":"10.1145\/2939672.2939778"},{"key":"11_CR21","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618\u2013626 (2017)","DOI":"10.1109\/ICCV.2017.74"},{"key":"11_CR22","unstructured":"Wang, Y., Zhang, T., Guo, X., Shen, Z.: Gradient based feature attribution in explainable ai: a technical review. arXiv preprint arXiv:2403.10415 (2024)"}],"container-title":["Communications in Computer and Information Science","Knowledge Management and Acquisition for Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-4575-9_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T23:02:03Z","timestamp":1763161323000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-4575-9_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819545742","9789819545759"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-4575-9_11","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"10 November 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"PKAW","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Principle and Practice of Data and Knowledge Acquisition Workshop","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Wellington","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"New Zealand","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 November 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 November 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pkaw2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/pkawwebsite.github.io\/2025","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}