{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T22:22:05Z","timestamp":1776723725104,"version":"3.51.2"},"reference-count":55,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2023,10,30]],"date-time":"2023-10-30T00:00:00Z","timestamp":1698624000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The inherent dynamism of recent technological advancements in intelligent vehicles has seen multitudes of noteworthy security concerns regarding interactions and data. As future mobility embraces the concept of vehicles-to-everything, it exacerbates security complexities and challenges concerning dynamism, adaptiveness, and self-awareness. It calls for a transition from security measures relying on static approaches and implementations. Therefore, to address this transition, this work proposes a hierarchical self-aware security architecture that effectively establishes accountability at the system level and further illustrates why such a proposed security architecture is relevant to intelligent vehicles. The article provides (1) a comprehensive understanding of the self-aware security concept, with emphasis on its hierarchical security architecture that enables system-level accountability, and (2) a deep dive into each layer supported by algorithms and a security-specific in-vehicle black box with external virtual security operation center (VSOC) interactions. In contrast to the present in-vehicle security measures, this architecture introduces characteristics and properties that enact self-awareness through system-level accountability. It implements hierarchical layers that enable real-time monitoring, analysis, decision-making, and in-vehicle and remote site integration regarding security-related decisions and activities.<\/jats:p>","DOI":"10.3390\/s23218817","type":"journal-article","created":{"date-parts":[[2023,10,30]],"date-time":"2023-10-30T13:26:55Z","timestamp":1698672415000},"page":"8817","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Self-Aware Cybersecurity Architecture for Autonomous Vehicles: Security through System-Level Accountability"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4532-7643","authenticated-orcid":false,"given":"Akwasi","family":"Adu-Kyere","sequence":"first","affiliation":[{"name":"Department of Computing, University of Turku, Vesilinnatie 5, 20500 Turku, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2043-4274","authenticated-orcid":false,"given":"Ethiopia","family":"Nigussie","sequence":"additional","affiliation":[{"name":"Department of Computing, University of Turku, Vesilinnatie 5, 20500 Turku, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5789-3992","authenticated-orcid":false,"given":"Jouni","family":"Isoaho","sequence":"additional","affiliation":[{"name":"Department of Computing, University of Turku, Vesilinnatie 5, 20500 Turku, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Yu, M., Guo, Z., Shen, S., Ning, Y., Liu, T., and Sun, D. 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