{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/know-your-master-driver-profiling-based-anti","title":"Know Your Master: Driver Profiling-based Anti-theft Method","arxiv_id":"1704.05223","date":"2017-04-18","proceeding":null,"authors":["Byung Il Kwak","JiYoung Woo","Huy Kang Kim"],"abstract":"Although many anti-theft technologies are implemented, auto-theft is still\nincreasing. Also, security vulnerabilities of cars can be used for auto-theft\nby neutralizing anti-theft system. This keyless auto-theft attack will be\nincreased as cars adopt computerized electronic devices more. To detect\nauto-theft efficiently, we propose the driver verification method that analyzes\ndriving patterns using measurements from the sensor in the vehicle. In our\nmodel, we add mechanical features of automotive parts that are excluded in\nprevious works, but can be differentiated by drivers' driving behaviors. We\ndesign the model that uses significant features through feature selection to\nreduce the time cost of feature processing and improve the detection\nperformance. Further, we enrich the feature set by deriving statistical\nfeatures such as mean, median, and standard deviation. This minimizes the\neffect of fluctuation of feature values per driver and finally generates the\nreliable model. We also analyze the effect of the size of sliding window on\nperformance to detect the time point when the detection becomes reliable and to\ninform owners the theft event as soon as possible. We apply our model with real\ndriving and show the contribution of our work to the literature of driver\nidentification.","url_abs":"http://arxiv.org/abs/1704.05223v1","url_pdf":"http://arxiv.org/pdf/1704.05223v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"know-your-master-driver-profiling-based-anti","repo_url":"https://github.com/yoshino0705/Driver_Behavior_Recognition","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"driver-identification","task_name":"Driver Identification"},{"task_slug":"feature-selection","task_name":"feature selection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}