{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T19:40:00Z","timestamp":1773344400167,"version":"3.50.1"},"reference-count":42,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"name":"European Union (EU) H2020 nIoVE","award":["833742"],"award-info":[{"award-number":["833742"]}]},{"DOI":"10.13039\/501100001858","name":"Cybersecurity KP project, and by the VINNOVA Fordonsstrategisk Forskning och Innovation (FFI) Project CyReV","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100003077","name":"Knowledge Foundation Sweden (KKS) Research Profile Next Generation Industrial IoT (NIIT) and by MERIT","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100003077","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2022]]},"DOI":"10.1109\/access.2022.3208573","type":"journal-article","created":{"date-parts":[[2022,9,22]],"date-time":"2022-09-22T23:01:19Z","timestamp":1663887679000},"page":"102831-102841","source":"Crossref","is-referenced-by-count":58,"title":["Experimental Analysis of Trustworthy In-Vehicle Intrusion Detection System Using eXplainable Artificial Intelligence (XAI)"],"prefix":"10.1109","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5367-4277","authenticated-orcid":false,"given":"Hampus","family":"Lundberg","sequence":"first","affiliation":[{"name":"Department of Information Systems & Technology, Mid Sweden University, Sundsvall, Sweden"}]},{"given":"Nishat I","family":"Mowla","sequence":"additional","affiliation":[{"name":"RISE, Lindholmspiren 3A, Gothenburg, Sweden"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2966-6469","authenticated-orcid":false,"given":"Sarder Fakhrul","family":"Abedin","sequence":"additional","affiliation":[{"name":"Department of Information Systems & Technology, Mid Sweden University, Sundsvall, Sweden"}]},{"given":"Kyi","family":"Thar","sequence":"additional","affiliation":[{"name":"Department of Information Systems & Technology, Mid Sweden University, Sundsvall, Sweden"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3717-7793","authenticated-orcid":false,"given":"Aamir","family":"Mahmood","sequence":"additional","affiliation":[{"name":"Department of Information Systems & Technology, Mid Sweden University, Sundsvall, Sweden"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0873-7827","authenticated-orcid":false,"given":"Mikael","family":"Gidlund","sequence":"additional","affiliation":[{"name":"Department of Information Systems & Technology, Mid Sweden University, Sundsvall, Sweden"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8192-0893","authenticated-orcid":false,"given":"Shahid","family":"Raza","sequence":"additional","affiliation":[{"name":"RISE, Lindholmspiren 3A, Gothenburg, Sweden"}]}],"member":"263","reference":[{"key":"ref1","volume-title":"Increasing the Trustworthiness of AI-Based in-Vehicle IDS using eXplainable AI","author":"Lundberg","year":"2022"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1186\/s13638-019-1484-3"},{"issue":"260","key":"ref3","first-page":"15","article-title":"Adventures in automotive networks and control units","volume":"21","author":"Valasek","year":"2013","journal-title":"Def Con"},{"key":"ref4","first-page":"2021","article-title":"Comprehensive experimental analyses of automotive attack surfaces","volume-title":"Proc. USENIX Secur. Symp.","volume":"4","author":"Checkoway"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2014.2342271"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.5220\/0005550003170326"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/PDP52278.2021.00050"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.3403\/30134702"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1049\/cce:19990304"},{"key":"ref10","article-title":"State-of-the-art survey on in-vehicle network communication (CAN-Bus) security and vulnerabilities","author":"Avatefipour","year":"2018","journal-title":"arXiv:1802.01725"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2018.2812149"},{"key":"ref12","first-page":"1","article-title":"A frequency-based data mining approach to enhance in-vehicle network intrusion detection","volume-title":"Proc. FAST-Zero","author":"Rosell"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.vehcom.2018.09.004"},{"key":"ref14","volume-title":"Worldwide Spending on Cognitive and Artificial Intelligence Systems Spending Guide","year":"2021"},{"key":"ref15","volume-title":"Revenues From the Artificial Intelligence (AI) Software Market Worldwide From 2018 to 2025","year":"2022"},{"key":"ref16","volume-title":"Intrusion detection systems: A survey and taxonomy","author":"Axelsson","year":"2000"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1002\/ett.4150"},{"key":"ref18","volume":"24","author":"Axelsson","year":"2006","journal-title":"Understanding Intrusion Detection Through Visualization"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.2478\/9788395669606-004"},{"key":"ref20","article-title":"Metrics for explainable AI: Challenges and prospects","author":"Hoffman","year":"2018","journal-title":"arXiv:1812.04608"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1145\/2594473.2594475"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1145\/3236386.3241340"},{"key":"ref23","article-title":"Towards a rigorous science of interpretable machine learning","author":"Doshi-Velez","year":"2017","journal-title":"arXiv:1702.08608"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2870052"},{"key":"ref25","volume-title":"Interpretable Machine Learning","author":"Molnar","year":"2020"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1609\/aimag.v38i3.2741"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1093\/oso\/9780198826491.001.0001"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1145\/3267338"},{"key":"ref29","first-page":"1","article-title":"A unified approach to interpreting model predictions","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Lundberg"},{"key":"ref30","article-title":"Explaining network intrusion detection system using explainable AI framework","author":"Mane","year":"2021","journal-title":"arXiv:2103.07110"},{"key":"ref31","article-title":"Explaining network intrusion detection system using explainable AI framework","author":"Mane","year":"2021","journal-title":"arXiv:2103.07110"},{"key":"ref32","article-title":"Domain knowledge aided explainable artificial intelligence for intrusion detection and response","author":"Rabiul Islam","year":"2019","journal-title":"arXiv:1911.09853"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/6634811"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.23919\/MIPRO.2018.8400040"},{"key":"ref35","article-title":"Addressing the lack of comparability & testing in CAN intrusion detection research: A comprehensive guide to CAN IDS data & introduction of the ROAD dataset","author":"Verma","year":"2022","journal-title":"arXiv:2012.14600"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.7249\/p0295"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N16-3020"},{"key":"ref38","first-page":"3145","article-title":"Learning important features through propagating activation differences","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Shrikumar"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0130140"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.9734\/BJAST\/2015\/14975"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.491"},{"key":"ref42","article-title":"TrustyAI explainability toolkit","author":"Geada","year":"2021","journal-title":"arXiv:2104.12717"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/9668973\/09899390.pdf?arnumber=9899390","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,22]],"date-time":"2024-01-22T20:57:33Z","timestamp":1705957053000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9899390\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"references-count":42,"URL":"https:\/\/doi.org\/10.1109\/access.2022.3208573","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]}}}