{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T14:02:19Z","timestamp":1774447339689,"version":"3.50.1"},"reference-count":40,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T00:00:00Z","timestamp":1767916800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000780","name":"European Commission","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems with Applications"],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1016\/j.eswa.2025.131039","type":"journal-article","created":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T08:05:50Z","timestamp":1767168350000},"page":"131039","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Intelligent resilience testing of vehicle control systems via learning-guided fuzzing"],"prefix":"10.1016","volume":"307","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-6591-7819","authenticated-orcid":false,"given":"G\u00e1bor","family":"L\u00f3r\u00e1nt","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6172-5772","authenticated-orcid":false,"given":"Zsolt","family":"Szalay","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1985-4095","authenticated-orcid":false,"given":"\u00c1rp\u00e1d","family":"T\u00f6r\u00f6k","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.eswa.2025.131039_bib0001","series-title":"Proceedings of the 36th international conference on machine learning","first-page":"111","article-title":"Online control with adversarial disturbances","volume":"vol. 97","author":"Agarwal","year":"2019"},{"key":"10.1016\/j.eswa.2025.131039_bib0002","series-title":"2022\u202fIEEE International conference on automation, quality and testing, robotics (AQTR)","first-page":"1","article-title":"Security analysis of vehicle instrument clusters by automatic fuzzing and image acquisition","author":"Anistoroaei","year":"2022"},{"key":"10.1016\/j.eswa.2025.131039_bib0003","series-title":"Technical Report","article-title":"AUTOSAR FO PRS Secure Onboard Communication Protocol","author":"AUTOSAR","year":"2024"},{"key":"10.1016\/j.eswa.2025.131039_bib0004","series-title":"Proceedings of the 2016\u202fACM SIGSAC conference on computer and communications security","first-page":"1032","article-title":"Coverage-based greybox fuzzing as markov chain","author":"B\u00f6hme","year":"2016"},{"key":"10.1016\/j.eswa.2025.131039_bib0005","series-title":"2018\u202fIEEE Security and privacy workshops (SPW)","first-page":"116","article-title":"Deep reinforcement fuzzing","author":"B\u00f6ttinger","year":"2018"},{"issue":"2","key":"10.1016\/j.eswa.2025.131039_bib0006","doi-asserted-by":"crossref","first-page":"832","DOI":"10.1109\/TIV.2024.3418887","article-title":"Adversarial stress test for autonomous vehicle via series reinforcement learning tasks with reward shaping","volume":"10","author":"Cai","year":"2025","journal-title":"IEEE Transactions on Intelligent Vehicles"},{"key":"10.1016\/j.eswa.2025.131039_bib0007","unstructured":"Cheng, S., Miao, Y., Dong, Y., Yang, X., Gao, X.-S., & Zhu, J. (2024). Efficient black-box adversarial attacks via bayesian optimization guided by a function prior. arxiv: 2405.19098."},{"key":"10.1016\/j.eswa.2025.131039_bib0008","unstructured":"Christodoulou, P. (2019). Soft actor-critic for discrete action settings. arxiv: 1910.07207."},{"key":"10.1016\/j.eswa.2025.131039_bib0009","doi-asserted-by":"crossref","DOI":"10.1016\/j.artint.2023.104057","article-title":"Evolving interpretable decision trees for reinforcement learning","volume":"327","author":"Costa","year":"2024","journal-title":"Artificial Intelligence"},{"key":"10.1016\/j.eswa.2025.131039_bib0010","series-title":"2018 21St international conference on intelligent transportation systems (ITSC)","first-page":"307","article-title":"Robust deep reinforcement learning for security and safety in autonomous vehicle systems","author":"Ferdowsi","year":"2018"},{"key":"10.1016\/j.eswa.2025.131039_bib0011","series-title":"14Th USENIX workshop on offensive technologies (WOOT 20)","article-title":"{AFL++}: Combining incremental steps of fuzzing research","author":"Fioraldi","year":"2020"},{"key":"10.1016\/j.eswa.2025.131039_bib0012","series-title":"2019\u202fIEEE 19Th international conference on software quality, reliability and security companion (QRS-c)","first-page":"1","article-title":"A method for constructing automotive cybersecurity tests, a CAN fuzz testing example","author":"Fowler","year":"2019"},{"issue":"10","key":"10.1016\/j.eswa.2025.131039_bib0013","doi-asserted-by":"crossref","first-page":"1585","DOI":"10.1080\/00423114.2012.693188","article-title":"Improvements to a five-phase ABS algorithm for experimental validation","volume":"50","author":"Gerard","year":"2012","journal-title":"Vehicle System Dynamics"},{"key":"10.1016\/j.eswa.2025.131039_bib0014","unstructured":"Haarnoja, T., Zhou, A., Hartikainen, K., Tucker, G., Ha, S., Tan, J., Kumar, V., Zhu, H., Gupta, A., Abbeel, P. et al. (2018). Soft actor-critic algorithms and applications. arxiv: 1812.05905."},{"key":"10.1016\/j.eswa.2025.131039_bib0015","doi-asserted-by":"crossref","first-page":"23259","DOI":"10.1109\/ACCESS.2022.3151358","article-title":"Efficient ECU analysis technology through structure-aware CAN fuzzing","volume":"10","author":"Kim","year":"2022","journal-title":"IEEE Access"},{"issue":"260","key":"10.1016\/j.eswa.2025.131039_bib0016","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1080\/01621459.1952.10483441","article-title":"Use of ranks in one-criterion variance analysis","volume":"47","author":"Kruskal","year":"1952","journal-title":"Journal of the American statistical Association"},{"key":"10.1016\/j.eswa.2025.131039_bib0017","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2020.114066","article-title":"Cosine similarity based anomaly detection methodology for the CAN bus","volume":"166","author":"Kwak","year":"2021","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2025.131039_bib0018","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.119771","article-title":"A survey of deep learning-based intrusion detection in automotive applications","volume":"221","author":"Lampe","year":"2023","journal-title":"Expert Systems with Applications"},{"issue":"1","key":"10.1016\/j.eswa.2025.131039_bib0019","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1186\/s42400-018-0002-y","article-title":"Fuzzing: A survey","volume":"1","author":"Li","year":"2018","journal-title":"Cybersecurity"},{"issue":"5","key":"10.1016\/j.eswa.2025.131039_bib0020","doi-asserted-by":"crossref","first-page":"3745","DOI":"10.1109\/TCYB.2020.3013675","article-title":"V-Fuzz: Vulnerability prediction-assisted evolutionary fuzzing for binary programs","volume":"52","author":"Li","year":"2022","journal-title":"IEEE Transactions on Cybernetics"},{"issue":"7","key":"10.1016\/j.eswa.2025.131039_bib0021","doi-asserted-by":"crossref","first-page":"6996","DOI":"10.3934\/mbe.2022330","article-title":"Gan model using field fuzz mutation for in-vehicle can bus intrusion detection","volume":"19","author":"Li","year":"2022","journal-title":"Mathematical Biosciences and Engineering"},{"issue":"325","key":"10.1016\/j.eswa.2025.131039_bib0022","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1080\/01621459.1969.10500983","article-title":"On the kolmogorov-smirnov test for the exponential distribution with mean unknown","volume":"64","author":"Lilliefors","year":"1969","journal-title":"Journal of the American Statistical Association"},{"key":"10.1016\/j.eswa.2025.131039_bib0023","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2024.110528","article-title":"Quantitative risk assessment for connected automated vehicles: Integrating improved STPA-safesec and bayesian network","volume":"253","author":"Liu","year":"2025","journal-title":"Reliability Engineering & System Safety"},{"key":"10.1016\/j.eswa.2025.131039_bib0024","series-title":"2021\u202fIEEE European symposium on security and privacy workshops (euros&PW)","first-page":"123","article-title":"Using cyber digital twins for automated automotive cybersecurity testing","author":"Marksteiner","year":"2021"},{"issue":"8","key":"10.1016\/j.eswa.2025.131039_bib0025","doi-asserted-by":"crossref","first-page":"3779","DOI":"10.1109\/TNNLS.2021.3121870","article-title":"Deep reinforcement learning for cyber security","volume":"34","author":"Nguyen","year":"2021","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"10.1016\/j.eswa.2025.131039_bib0026","series-title":"Proceedings of the ACM workshop on automotive cybersecurity","first-page":"41","article-title":"Hardware-in-loop based automotive embedded systems cybersecurity evaluation testbed","author":"Oruganti","year":"2019"},{"key":"10.1016\/j.eswa.2025.131039_bib0027","series-title":"Tire and vehicle dynamics","author":"Pacejka","year":"2005"},{"key":"10.1016\/j.eswa.2025.131039_bib0028","series-title":"2024\u202fIEEE 32Nd international conference on network protocols (ICNP)","first-page":"1","article-title":"Rlgfuzz: Reinforcement learning guided fuzzing with state-coverage mapping environment","author":"Shen","year":"2024"},{"key":"10.1016\/j.eswa.2025.131039_bib0029","series-title":"Reinforcement learning: An introduction","volume":"vol. 1","author":"Sutton","year":"1998"},{"issue":"3","key":"10.1016\/j.eswa.2025.131039_bib0030","doi-asserted-by":"crossref","first-page":"2497","DOI":"10.1007\/s10462-022-10228-y","article-title":"Monte carlo tree search: A review of recent modifications and applications","volume":"56","author":"\u015awiechowski","year":"2023","journal-title":"Artificial Intelligence Review"},{"issue":"4","key":"10.1016\/j.eswa.2025.131039_bib0031","doi-asserted-by":"crossref","first-page":"2551","DOI":"10.1109\/TIV.2024.3379367","article-title":"Explainable deep adversarial reinforcement learning approach for robust autonomous driving","volume":"10","author":"Wang","year":"2025","journal-title":"IEEE Transactions on Intelligent Vehicles"},{"issue":"3","key":"10.1016\/j.eswa.2025.131039_bib0032","doi-asserted-by":"crossref","first-page":"1411","DOI":"10.1109\/TITS.2020.2970295","article-title":"Real-time sensor anomaly detection and recovery in connected automated vehicle sensors","volume":"22","author":"Wang","year":"2020","journal-title":"IEEE transactions on intelligent transportation systems"},{"key":"10.1016\/j.eswa.2025.131039_bib0033","doi-asserted-by":"crossref","DOI":"10.1016\/j.jnca.2024.104020","article-title":"A survey on fuzz testing technologies for industrial control protocols","volume":"232","author":"Wei","year":"2024","journal-title":"Journal of Network and Computer Applications"},{"key":"10.1016\/j.eswa.2025.131039_bib0034","doi-asserted-by":"crossref","first-page":"169370","DOI":"10.1109\/ACCESS.2024.3452938","article-title":"A control flow graph optimization method for enhancing fuzz testing","volume":"12","author":"Yuan","year":"2024","journal-title":"IEEE Access"},{"key":"10.1016\/j.eswa.2025.131039_bib0035","unstructured":"Zalewski, M. (2016). American fuzzy lop (AFL) fuzzer. https:\/\/lcamtuf.coredump.cx\/afl https:\/\/lcamtuf.coredump.cx\/afl."},{"key":"10.1016\/j.eswa.2025.131039_bib0036","series-title":"2021 International conference on computer information science and artificial intelligence (CISAI)","first-page":"225","article-title":"Can-ft: A fuzz testing method for automotive controller area network bus","author":"Zhang","year":"2021"},{"key":"10.1016\/j.eswa.2025.131039_bib0037","doi-asserted-by":"crossref","first-page":"5735","DOI":"10.1109\/TIFS.2023.3314219","article-title":"How to disturb network reconnaissance: A moving target defense approach based on deep reinforcement learning","volume":"18","author":"Zhang","year":"2023","journal-title":"IEEE Transactions on Information Forensics and Security"},{"key":"10.1016\/j.eswa.2025.131039_bib0038","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2025.110822","article-title":"Safety risk assessment for connected and automated vehicles: Integrating FTA and CM-improved AHP","volume":"257","author":"Zheng","year":"2025","journal-title":"Reliability Engineering & System Safety"},{"issue":"11s","key":"10.1016\/j.eswa.2025.131039_bib0039","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3512345","article-title":"Fuzzing: A survey for roadmap","volume":"54","author":"Zhu","year":"2022","journal-title":"ACM Computing Surveys (CSUR)"},{"key":"10.1016\/j.eswa.2025.131039_bib0040","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijcip.2025.100808","article-title":"From antagonisms to synergies: A systematic review of safety-security interrelations","volume":"51","author":"Zimmermann","year":"2025","journal-title":"International Journal of Critical Infrastructure Protection"}],"container-title":["Expert Systems with Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417425046536?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417425046536?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T12:20:01Z","timestamp":1774441201000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0957417425046536"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4]]},"references-count":40,"alternative-id":["S0957417425046536"],"URL":"https:\/\/doi.org\/10.1016\/j.eswa.2025.131039","relation":{},"ISSN":["0957-4174"],"issn-type":[{"value":"0957-4174","type":"print"}],"subject":[],"published":{"date-parts":[[2026,4]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Intelligent resilience testing of vehicle control systems via learning-guided fuzzing","name":"articletitle","label":"Article Title"},{"value":"Expert Systems with Applications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.eswa.2025.131039","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"131039"}}