{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T23:25:50Z","timestamp":1768778750722,"version":"3.49.0"},"reference-count":14,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2019,5,1]],"date-time":"2019-05-01T00:00:00Z","timestamp":1556668800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"<jats:p>The widespread use of smartphones has dictated a new paradigm, where mobile applications are the primary channel for dealing with day-to-day tasks. This paradigm is full of sensitive information, making security of utmost importance. To that end, and given the traditional authentication techniques (passwords and\/or unlock patterns) which have become ineffective, several research efforts are targeted towards biometrics security, while more advanced techniques are considering continuous implicit authentication on the basis of behavioral biometrics. However, most studies in this direction are performed \u201cin vitro\u201d resulting in small-scale experimentation. In this context, and in an effort to create a solid information basis upon which continuous authentication models can be built, we employ the real-world application \u201cBrainRun\u201d, a brain-training game aiming at boosting cognitive skills of individuals. BrainRun embeds a gestures capturing tool, so that the different types of gestures that describe the swiping behavior of users are recorded and thus can be modeled. Upon releasing the application at both the \u201cGoogle Play Store\u201d and \u201cApple App Store\u201d, we construct a dataset containing gestures and sensors data for more than 2000 different users and devices. The dataset is distributed under the CC0 license and can be found at the EU Zenodo repository.<\/jats:p>","DOI":"10.3390\/data4020060","type":"journal-article","created":{"date-parts":[[2019,5,2]],"date-time":"2019-05-02T03:15:22Z","timestamp":1556766922000},"page":"60","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["BrainRun: A Behavioral Biometrics Dataset towards Continuous Implicit Authentication"],"prefix":"10.3390","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8973-0293","authenticated-orcid":false,"given":"Michail D.","family":"Papamichail","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0715-1197","authenticated-orcid":false,"given":"Kyriakos C.","family":"Chatzidimitriou","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6117-8222","authenticated-orcid":false,"given":"Thomas","family":"Karanikiotis","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1887-799X","authenticated-orcid":false,"given":"Napoleon-Christos I.","family":"Oikonomou","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0235-6046","authenticated-orcid":false,"given":"Andreas L.","family":"Symeonidis","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece"}]},{"given":"Sashi K.","family":"Saripalle","sequence":"additional","affiliation":[{"name":"ZOLOZ, Kansas City, MO 64108, USA"}]}],"member":"1968","published-online":{"date-parts":[[2019,5,1]]},"reference":[{"key":"ref_1","unstructured":"(2019, April 19). Number of Mobile Phone Users. Available online: https:\/\/www.statista.com\/statistics\/274774\/forecast-of-mobile-phone-users-worldwide\/."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1998","DOI":"10.1109\/COMST.2016.2537748","article-title":"Authentication of Smartphone Users Using Behavioral Biometrics","volume":"18","author":"Alzubaidi","year":"2016","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Gong, N.Z., Payer, M., Moazzezi, R., and Frank, M. (2015). Towards Forgery-Resistant Touch-based Biometric Authentication on Mobile Devices. CoRR, abs\/1506.02294.","DOI":"10.1145\/2897845.2897908"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Egelman, S., Jain, S., Portnoff, R.S., Liao, K., Consolvo, S., and Wagner, D. (2014, January 3\u20137). Are You Ready to Lock? Understanding User Motivations for Smartphone Locking Behaviors. 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Secur."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1810","DOI":"10.1109\/ACCESS.2016.2557846","article-title":"Learning Human Identity From Motion Patterns","volume":"4","author":"Neverova","year":"2016","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"De Luca, A., Hang, A., Brudy, F., Lindner, C., and Hussmann, H. (2012, January 5\u201310). Touch Me Once and I Know It\u2019s You!: Implicit Authentication Based on Touch Screen Patterns. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Austin, TX, USA.","DOI":"10.1145\/2207676.2208544"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Mahbub, U., Sarkar, S., Patel, V.M., and Chellappa, R. (2016, January 6\u20139). Active user authentication for smartphones: A challenge data set and benchmark results. 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CoRR, abs\/1207.6231."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.jnca.2018.02.020","article-title":"Continuous Authentication of Smartphone Users Based on Activity Pattern Recognition Using Passive Mobile Sensing","volume":"109","author":"Naeem","year":"2018","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Lee, W., and Lee, R.B. (2017, January 26\u201329). Sensor-Based Implicit Authentication of Smartphone Users. Proceedings of the 2017 47th Annual IEEE\/IFIP International Conference on Dependable Systems and Networks (DSN), Denver, CO, USA.","DOI":"10.1109\/DSN.2017.21"}],"container-title":["Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5729\/4\/2\/60\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:48:29Z","timestamp":1760186909000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5729\/4\/2\/60"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,5,1]]},"references-count":14,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2019,6]]}},"alternative-id":["data4020060"],"URL":"https:\/\/doi.org\/10.3390\/data4020060","relation":{},"ISSN":["2306-5729"],"issn-type":[{"value":"2306-5729","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,5,1]]}}}