{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T22:59:30Z","timestamp":1773269970346,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":50,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819562022","type":"print"},{"value":"9789819562039","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-981-95-6203-9_11","type":"book-chapter","created":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T02:29:10Z","timestamp":1767320950000},"page":"195-214","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["LogWhisperer: Multi-log Semantic Similarity Analysis Based Intelligent Vehicle Anomaly Detection Without Log Template"],"prefix":"10.1007","author":[{"given":"Hongyi","family":"Guo","sequence":"first","affiliation":[]},{"given":"Kun","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Kui","family":"Ren","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,2]]},"reference":[{"key":"11_CR1","unstructured":"Aafer, Y., You, W., Sun, Y., Shi, Y., Zhang, X., Yin, H.: Android $$\\{\\text{SmartTVs}\\}$$ vulnerability discovery via $$\\{\\text{ log-guided }\\}$$ fuzzing. In: 30th USENIX Security Symposium (USENIX Security 2021), pp. 2759\u20132776 (2021)"},{"issue":"1","key":"11_CR2","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/S0306-4573(02)00021-3","volume":"39","author":"A Aizawa","year":"2003","unstructured":"Aizawa, A.: An information-theoretic perspective of TF-IDF measures. Inf. Process. Manage. 39(1), 45\u201365 (2003)","journal-title":"Inf. Process. Manage."},{"key":"11_CR3","unstructured":"Android\u00a0Debug Bridge. Android debug bridge. Android (operating system)-Unabridged Guide, p.\u00a069 (2012)"},{"issue":"4","key":"11_CR4","first-page":"1","volume":"54","author":"B Chen","year":"2021","unstructured":"Chen, B., Jiang, Z.M.: A survey of software log instrumentation. ACM Comput. Surv. (CSUR) 54(4), 1\u201334 (2021)","journal-title":"ACM Comput. Surv. (CSUR)"},{"issue":"2","key":"11_CR5","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1109\/MCOMSTD.2017.1700015","volume":"1","author":"S Chen","year":"2017","unstructured":"Chen, S., et al.: Vehicle-to-everything (v2x) services supported by LTE-based systems and 5g. IEEE Commun. Standards Mag. 1(2), 70\u201376 (2017)","journal-title":"IEEE Commun. Standards Mag."},{"key":"11_CR6","doi-asserted-by":"crossref","unstructured":"Chen, Z., Yang, K., Ren, K.: CDS: an anti-aging calibratable digital sensor for detecting multiple types of fault injection attacks. In: Proceedings of the 61st ACM\/IEEE Design Automation Conference, pp. 1\u20136 (2024)","DOI":"10.1145\/3649329.3657322"},{"key":"11_CR7","doi-asserted-by":"crossref","unstructured":"Du, M., Li, F., Zheng, G., Srikumar, V.: Deeplog: anomaly detection and diagnosis from system logs through deep learning. In: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, pp. 1285\u20131298 (2017)","DOI":"10.1145\/3133956.3134015"},{"issue":"1","key":"11_CR8","doi-asserted-by":"publisher","first-page":"135","DOI":"10.32604\/csse.2021.014030","volume":"37","author":"X Duan","year":"2021","unstructured":"Duan, X., Ying, S., Yuan, W., Cheng, H., Yin, X.: A generative adversarial networks for log anomaly detection. Comput. Syst. Sci. Eng. 37(1), 135\u2013148 (2021)","journal-title":"Comput. Syst. Sci. Eng."},{"key":"11_CR9","doi-asserted-by":"crossref","unstructured":"Ekberg, J.-E., Kostiainen, K., Asokan, N.: Trusted execution environments on mobile devices. In: Proceedings of the 2013 ACM SIGSAC Conference on Computer & Communications Security, pp. 1497\u20131498 (2013)","DOI":"10.1145\/2508859.2516758"},{"key":"11_CR10","doi-asserted-by":"crossref","unstructured":"Feng, S., Yan, X., Sun, H., Feng, Y., Liu, H.X.: Intelligent driving intelligence test for autonomous vehicles with naturalistic and adversarial environment. Nat. Commun. 12(1), 748 (2021)","DOI":"10.1038\/s41467-021-21007-8"},{"key":"11_CR11","doi-asserted-by":"crossref","unstructured":"Goyal, A., Han, X., Wang, G., Bates, A.: Sometimes, you aren\u2019t what you do: mimicry attacks against provenance graph host intrusion detection systems. In: 30th Network and Distributed System Security Symposium (2023)","DOI":"10.14722\/ndss.2023.24207"},{"key":"11_CR12","unstructured":"Goyal, A., Liu, J., Bates, A., Wang, G.: Orchid: streaming threat detection over versioned provenance graphs. arXiv preprint arXiv:2408.13347 (2024)"},{"issue":"2","key":"11_CR13","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1002\/cpa.20350","volume":"64","author":"M Grasmair","year":"2011","unstructured":"Grasmair, M., Scherzer, O., Haltmeier, M.: Necessary and sufficient conditions for linear convergence of l1-regularization. Commun. Pure Appl. Math. 64(2), 161\u2013182 (2011)","journal-title":"Commun. Pure Appl. Math."},{"key":"11_CR14","doi-asserted-by":"crossref","unstructured":"He, S., He, P., Chen, Z., Yang, T., Su, Y., Lyu, M.R.: A survey on automated log analysis for reliability engineering. ACM Comput. Surv. (CSUR) 54(6), 1\u201337 (2021)","DOI":"10.1145\/3460345"},{"key":"11_CR15","doi-asserted-by":"crossref","unstructured":"He, S., Zhu, J., He, P., Lyu, M.R.: Experience report: system log analysis for anomaly detection. In: 2016 IEEE 27th International Symposium on Software Reliability Engineering (ISSRE), pp. 207\u2013218. IEEE (2016)","DOI":"10.1109\/ISSRE.2016.21"},{"key":"11_CR16","unstructured":"He, S., Zhu, J., He, P., Lyu, M.R.: Loghub: a large collection of system log datasets towards automated log analytics. arXiv preprint arXiv:2008.06448 (2020)"},{"key":"11_CR17","unstructured":"Hofbauer, J., Buquerin, K.K.G., Hof, H.-J.: From SOC to VSOC: transferring key requirements for efficient vehicle security operations. Ruhr-Universit\u00e4t Bochum (2023)"},{"key":"11_CR18","unstructured":"Hu, S., Chen, Q.A., Sun, J., Feng, Y., Morley Mao, Z., Liu, H.X.: Automated discovery of $$\\{$$Denial-of-Service$$\\}$$ vulnerabilities in connected vehicle protocols. In: 30th USENIX Security Symposium (USENIX Security 2021), pp. 3219\u20133236 (2021)"},{"issue":"4","key":"11_CR19","doi-asserted-by":"publisher","first-page":"2064","DOI":"10.1109\/TNSM.2020.3034647","volume":"17","author":"S Huang","year":"2020","unstructured":"Huang, S., et al.: Hitanomaly: hierarchical transformers for anomaly detection in system log. IEEE Trans. Netw. Serv. Manage. 17(4), 2064\u20132076 (2020)","journal-title":"IEEE Trans. Netw. Serv. Manage."},{"key":"11_CR20","unstructured":"Fortune\u00a0Business Insights. Autonomous vehicle market size, share & Covid-19 impact analysis, by level (l1, l2, & l3 and l4 & l5), by vehicle type (passenger cars and commercial vehicles), and regional forecast, 2023\u20132030. Technical report (2022). www.fortunebusinessinsights.com\/autonomous-vehicle-market-109045"},{"key":"11_CR21","unstructured":"Spherical Insights. Global automotive industry market size, share, and covid-19 impact analysis, by type (commercial vehicle (lcvs, heavy trucks, buses & coaches), passenger car (hatchback, sedan, suv, muv)), by propulsion type (electric vehicle, ice vehicle), and by region (north america, europe, asia-pacific, latin america, middle east, and africa), analysis and forecast 2023\u20132033. Technical report (2024). www.sphericalinsights.com\/reports\/automotive-industry-market"},{"key":"11_CR22","doi-asserted-by":"crossref","unstructured":"Jansen, K., Sch\u00e4fer, M., Moser, D., Lenders, V., P\u00f6pper, C., Schmitt, J.: Crowd-GPS-sec: leveraging crowdsourcing to detect and localize GPS spoofing attacks. In: 2018 IEEE Symposium on Security and Privacy (SP), pp. 1018\u20131031. IEEE (2018)","DOI":"10.1109\/SP.2018.00012"},{"key":"11_CR23","doi-asserted-by":"crossref","unstructured":"Jia, T., Chen, P., Yang, L., Li, Y., Meng, F., Xu, J.: An approach for anomaly diagnosis based on hybrid graph model with logs for distributed services. In: 2017 IEEE International Conference on Web Services (ICWS), pp. 25\u201332. IEEE (2017)","DOI":"10.1109\/ICWS.2017.12"},{"issue":"6","key":"11_CR24","first-page":"1930","volume":"2","author":"AG Jivani","year":"2011","unstructured":"Jivani, A.G., et al.: A comparative study of stemming algorithms. Int. J. Comput. Tech. Appl. 2(6), 1930\u20131938 (2011)","journal-title":"Int. J. Comput. Tech. Appl."},{"issue":"5","key":"11_CR25","doi-asserted-by":"publisher","first-page":"3061","DOI":"10.3390\/app13053061","volume":"13","author":"S Karagiannis","year":"2023","unstructured":"Karagiannis, S., Ribeiro, L.L., Ntantogian, C., Magkos, E., Campos, L.M.: Chidroid: a mobile Android application for log collection and security analysis in healthcare and IoMT. Appl. Sci. 13(5), 3061 (2023)","journal-title":"Appl. Sci."},{"key":"11_CR26","doi-asserted-by":"crossref","unstructured":"Li, X., Meng, Y., Sun, X., Han, Q., Yuan, A., Li, J.: Is word segmentation necessary for deep learning of Chinese representations? arXiv preprint arXiv:1905.05526 (2019)","DOI":"10.18653\/v1\/P19-1314"},{"issue":"3","key":"11_CR27","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1109\/MWC.2019.1800289","volume":"26","author":"Y Li","year":"2019","unstructured":"Li, Y., Luo, Q., Liu, J., Guo, H., Kato, N.: Tsp security in intelligent and connected vehicles: challenges and solutions. IEEE Wirel. Commun. 26(3), 125\u2013131 (2019)","journal-title":"IEEE Wirel. Commun."},{"issue":"1","key":"11_CR28","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.jnca.2012.09.004","volume":"36","author":"H-J Liao","year":"2013","unstructured":"Liao, H.-J., Lin, C.-H.R., Lin, Y.-C., Tung, K.-Y.: Intrusion detection system: a comprehensive review. J. Netw. Comput. Appl. 36(1), 16\u201324 (2013)","journal-title":"J. Netw. Comput. Appl."},{"issue":"6","key":"11_CR29","doi-asserted-by":"publisher","first-page":"975","DOI":"10.1049\/iet-its.2018.5465","volume":"13","author":"Y Liu","year":"2019","unstructured":"Liu, Y., Wu, J., Li, J., Yang, W., Chen, H., Li, G.: ISRF: interest semantic reasoning based fog firewall for information-centric internet of vehicles. IET Intel. Transport Syst. 13(6), 975\u2013982 (2019)","journal-title":"IET Intel. Transport Syst."},{"key":"11_CR30","doi-asserted-by":"crossref","unstructured":"Lou, J.-G., Fu, Q., Yang, S., Li, J., Wu, B.: Mining program workflow from interleaved traces. In: Proceedings of the 16th ACM SIGKDD, pp. 613\u2013622 (2010)","DOI":"10.1145\/1835804.1835883"},{"key":"11_CR31","doi-asserted-by":"crossref","unstructured":"Lu, S., Wei, X., Li, Y., Wang, L.: Detecting anomaly in big data system logs using convolutional neural network. In: 2018 DASC\/PiCom\/DataCom\/CyberSciTech, pp. 151\u2013158. IEEE (2018)","DOI":"10.1109\/DASC\/PiCom\/DataCom\/CyberSciTec.2018.00037"},{"key":"11_CR32","unstructured":"Luo, M., Myers, A.C., Edward Suh, G.: Stealthy tracking of autonomous vehicles with cache side channels. In: 29th USENIX Security Symposium (USENIX Security 2020), pp. 859\u2013876 (2020)"},{"key":"11_CR33","doi-asserted-by":"crossref","unstructured":"Meng, W., et al.: Loganomaly: unsupervised detection of sequential and quantitative anomalies in unstructured logs. In: IJCAI, vol. 19, pp. 4739\u20134745 (2019)","DOI":"10.24963\/ijcai.2019\/658"},{"key":"11_CR34","doi-asserted-by":"crossref","unstructured":"Micinski, K., Votipka, D., Stevens, R., Kofinas, N., Mazurek, M.L., Foster, J.S.: User interactions and permission use on Android. In: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, pp. 362\u2013373 (2017)","DOI":"10.1145\/3025453.3025706"},{"key":"11_CR35","doi-asserted-by":"crossref","unstructured":"Ng, A.Y.: Feature selection, l 1 vs. l 2 regularization, and rotational invariance. In: Proceedings of the Twenty-First International Conference on Machine Learning, p.\u00a078 (2004)","DOI":"10.1145\/1015330.1015435"},{"key":"11_CR36","unstructured":"Nguyen, K.-T., et\u00a0al.: Vehicle re-identification with learned representation and spatial verification and abnormality detection with multi-adaptive vehicle detectors for traffic video analysis. In: CVPR Workshops, pp. 363\u2013372 (2019)"},{"key":"11_CR37","doi-asserted-by":"crossref","unstructured":"Oprea, A., Li, Z., Yen, T.-F., Chin, S.H., Alrwais, S.: Detection of early-stage enterprise infection by mining large-scale log data. In: 2015 45th Annual IEEE\/IFIP International Conference on Dependable Systems and Networks, pp. 45\u201356. IEEE (2015)","DOI":"10.1109\/DSN.2015.14"},{"issue":"1","key":"11_CR38","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1109\/TWC.2022.3192225","volume":"22","author":"D Orlando","year":"2022","unstructured":"Orlando, D., Bartoletti, S., Palam\u00e0, I., Bianchi, G., Melazzi, N.B.: Innovative attack detection solutions for wireless networks with application to location security. IEEE Trans. Wirel. Commun. 22(1), 205\u2013219 (2022)","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"11_CR39","doi-asserted-by":"crossref","unstructured":"Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532\u20131543 (2014)","DOI":"10.3115\/v1\/D14-1162"},{"issue":"2","key":"11_CR40","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1109\/JIOT.2014.2368356","volume":"2","author":"MA Salahuddin","year":"2014","unstructured":"Salahuddin, M.A., Al-Fuqaha, A., Guizani, M.: Software-defined networking for RSU clouds in support of the internet of vehicles. IEEE Internet Things J. 2(2), 133\u2013144 (2014)","journal-title":"IEEE Internet Things J."},{"key":"11_CR41","volume":"21","author":"HM Song","year":"2020","unstructured":"Song, H.M., Woo, J., Kim, H.K.: In-vehicle network intrusion detection using deep convolutional neural network. Veh. Commun. 21, 100198 (2020)","journal-title":"Veh. Commun."},{"key":"11_CR42","unstructured":"Song, R., Ozmen, M.O., Kim, H., Muller, R., Berkay Celik, Z., Bianchi, A.: Discovering adversarial driving maneuvers against autonomous vehicles. In: 32nd USENIX Security Symposium (USENIX Security 2023), pp. 2957\u20132974 (2023)"},{"key":"11_CR43","doi-asserted-by":"crossref","unstructured":"Song, X., Salcianu, A., Song, Y., Dopson, D., Zhou, D.: Fast wordpiece tokenization. arXiv preprint arXiv:2012.15524 (2020)","DOI":"10.18653\/v1\/2021.emnlp-main.160"},{"key":"11_CR44","doi-asserted-by":"crossref","unstructured":"Tang, L., Li, T., Perng, C.-S.: Logsig: generating system events from raw textual logs. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, pp. 785\u2013794 (2011)","DOI":"10.1145\/2063576.2063690"},{"key":"11_CR45","unstructured":"Callisto Technology. 2023 intelligent vehicle cybersecurity threat report. Technical report (2023). www.callisto-auto.com\/Report-2023-CN.pdf"},{"key":"11_CR46","unstructured":"Wu, J., Nan, Y., Kumar, V., Payer, M., Xu, D.: $$\\{\\text{ BlueShield }\\}$$: detecting spoofing attacks in bluetooth low energy networks. In: 23rd International Symposium on Research in Attacks, Intrusions and Defenses (RAID 2020), pp. 397\u2013411 (2020)"},{"key":"11_CR47","doi-asserted-by":"crossref","unstructured":"Wu, J., Wu, R., Xu, D., Tian, D.J., Bianchi, A.: Formal model-driven discovery of bluetooth protocol design vulnerabilities. In: 2022 IEEE Symposium on Security and Privacy (SP), pp. 2285\u20132303. IEEE (2022)","DOI":"10.1109\/SP46214.2022.9833777"},{"key":"11_CR48","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1007\/s10796-020-10026-3","volume":"23","author":"B Xia","year":"2021","unstructured":"Xia, B., Bai, Y., Yin, J., Li, Y., Jian, X.: Loggan: a log-level generative adversarial network for anomaly detection using permutation event modeling. Inf. Syst. Front. 23, 285\u2013298 (2021)","journal-title":"Inf. Syst. Front."},{"key":"11_CR49","doi-asserted-by":"crossref","unstructured":"Yen, S., Moh, M., Moh, T.-S.: Causalconvlstm: semi-supervised log anomaly detection through sequence modeling. In: 2019 18th IEEE International Conference On Machine Learning and Applications (ICMLA), pp. 1334\u20131341. IEEE (2019)","DOI":"10.1109\/ICMLA.2019.00217"},{"key":"11_CR50","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2022.104589","volume":"144","author":"K You","year":"2022","unstructured":"You, K., Peng, G., Ding, L., Dou, Q., Zhangang, W., Zhou, C.: Smart t-box of unmanned earthwork machinery for internet of vehicles. Autom. Constr. 144, 104589 (2022)","journal-title":"Autom. Constr."}],"container-title":["Lecture Notes in Computer Science","Information Security and Cryptology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-6203-9_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T07:37:33Z","timestamp":1773214653000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-6203-9_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819562022","9789819562039"],"references-count":50,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-6203-9_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"2 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"Inscrypt","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Information Security and Cryptology","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Xi'an","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":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 October 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":"cisc22025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/inscrypt2025.xidian.edu.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}