{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,22]],"date-time":"2025-12-22T08:08:45Z","timestamp":1766390925551,"version":"build-2065373602"},"reference-count":18,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2020,3,18]],"date-time":"2020-03-18T00:00:00Z","timestamp":1584489600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT)","award":["No. 2018R1A4A1025632"],"award-info":[{"award-number":["No. 2018R1A4A1025632"]}]},{"name":"Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education","award":["NRF-2018R1D1A1B07047656"],"award-info":[{"award-number":["NRF-2018R1D1A1B07047656"]}]},{"DOI":"10.13039\/501100002560","name":"Soonchunhyang University","doi-asserted-by":"publisher","award":["Research Fund"],"award-info":[{"award-number":["Research Fund"]}],"id":[{"id":"10.13039\/501100002560","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>The objective of this study was to verify the feasibility of mouse data exposure by deriving features to improve the accuracy of a mouse data attack technique using machine learning models. To improve the accuracy, the feature appearing between the mouse coordinates input from the user was analyzed, which is defined as a feature for machine learning models to derive a method of improving the accuracy. As a result, we found a feature where the distance between the coordinates is concentrated in a specific range. We verified that the mouse data is apt to being stolen more accurately when the distance is used as a feature. An accuracy of over 99% was achieved, which means that the proposed method almost completely classifies the mouse data input from the user and the mouse data generated by the defender.<\/jats:p>","DOI":"10.3390\/e22030355","type":"journal-article","created":{"date-parts":[[2020,3,19]],"date-time":"2020-03-19T03:54:14Z","timestamp":1584590054000},"page":"355","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Improved Practical Vulnerability Analysis of Mouse Data According to Offensive Security based on Machine Learning in Image-Based User Authentication"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1477-7569","authenticated-orcid":false,"given":"Kyungroul","family":"Lee","sequence":"first","affiliation":[{"name":"R&amp;BD Center for Security and Safety Industries (SSI), Soonchunhyang University, Asan-si, Chungnam 31538, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4686-9436","authenticated-orcid":false,"given":"Sun-Young","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Information Security Engineering, Soonchunhyang University, Asan-si, Chungnam 31538, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,3,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Conklin, A., Dietrich, G., and Walz, D. (2004, January 5\u20138). Password-based authentication: A system perspective. Proceedings of the 37th Annual Hawaii International Conference on System Sciences, Big Island, HI, USA.","DOI":"10.1109\/HICSS.2004.1265412"},{"key":"ref_2","unstructured":"Lee, K., and Yim, K. (2010, January 5\u20136). Password sniff by forcing the keyboard to replay scan codes. Proceedings of the Joint Workshop Information Security, Guangzhou, China."},{"key":"ref_3","unstructured":"Lee, K., and Yim, K. (July, January 30). Keyboard security: A technological review. Proceedings of the International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, Seoul, Korea."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Oh, I., Lee, K., Lee, S., Do, K., Ahn, H., and Yim, K. (2018, January 4\u20136). Vulnerability Analysis on the Image-Based Authentication Through the PS\/2 Interface. Proceedings of the International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, Matsue, Japan.","DOI":"10.1007\/978-3-319-93554-6_19"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Takada, T., and Koike, H. (2003, January 8\u201311). Awase-E: Image-based authentication for mobile phones using user\u2019s favorite images. Proceedings of the International Conference on Mobile Human-Computer Interaction, Udine, Italy.","DOI":"10.1007\/978-3-540-45233-1_26"},{"key":"ref_6","first-page":"534","article-title":"Secure authentication using anti-screenshot virtual keyboard","volume":"8","author":"Parekh","year":"2011","journal-title":"Int. J. Comput. Sci. Issues"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Newman, R.E., Harsh, P., and Jayaraman, P. (2005, January 11\u201314). Security analysis of and proposal for image-based authentication. 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A Protection Technique for Screen Image-based Authentication Protocols Utilizing the SetCursorPos function. Proceedings of the World conference on Information Security Applications, Jeju Island, Korea."},{"key":"ref_11","unstructured":"Lee, K., and Yim, K. (2017, January 21\u201323). Vulnerability Analysis on the Image-based Authentication: Through the WM_INPUT message. 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Intell."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"8682","DOI":"10.1109\/ACCESS.2017.2705644","article-title":"Sensor Fault Classification Based on Support Vector Machine and Statistical Time-Domain Features","volume":"5","author":"Jan","year":"2017","journal-title":"IEEE Access"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"21954","DOI":"10.1109\/ACCESS.2017.2762418","article-title":"A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks","volume":"5","author":"Yin","year":"2017","journal-title":"IEEE Access"}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/22\/3\/355\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:07:50Z","timestamp":1760173670000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/22\/3\/355"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,18]]},"references-count":18,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2020,3]]}},"alternative-id":["e22030355"],"URL":"https:\/\/doi.org\/10.3390\/e22030355","relation":{},"ISSN":["1099-4300"],"issn-type":[{"type":"electronic","value":"1099-4300"}],"subject":[],"published":{"date-parts":[[2020,3,18]]}}}