[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127786-en":3,"doc-seo-127786-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127786,1099523885074,"Ivy","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",6,"Technology","YOUR DEVICE MAY KNOW YOU BETTER THAN YOU KNOW YOURSELF - CONTINUOUS AUTHENTICATION ON NOVEL DATASET","Research advances continuous authentication using behavioral biometrics by providing a novel dataset built from touchscreen gesture data recorded from 15 users playing Minecraft on a Samsung tablet for 15 minutes each. Machine learning binary classifiers—Random Forest, K-Nearest Neighbors, and Support Vector Classifier—are trained to determine the authenticity of specific user actions. The strongest model is SVC, reaching about 90% average accuracy, showing touch dynamics can distinguish users, while further work is required for deployment in authentication systems. The dataset is publicly available via the provided repository link.","YOUR DEVICE MAY KNOW YOU BETTER THAN YOU KNOW YOURSELF-CONTINUOUS  \nAUTHENTICATION ON NOVEL DATASET USING  \nMACHINE LEARNING  \nPedro Gomes do Nascimento, Pidge Witiak, Tucker MacCallum, Zachary  \nWinterfeldt, Rushit Dave  \nDepartment of Computer Information Science, Minnesota State University, Mankato,  \nMankato, USA  \n[pedro.gomesdonascimento@mnsu.edu](pedro.gomesdonascimento@mnsu.edu)  \n[pidge.witiak@mnsu.edu](pidge.witiak@mnsu.edu)  \n[tucker.maccallum@mnsu.edu](tucker.maccallum@mnsu.edu)  \n[zachary.winterfeldt@mnsu.edu](zachary.winterfeldt@mnsu.edu)  \n[rushit.dave@mnsu.edu](rushit.dave@mnsu.edu)  \nABSTRACT  \nThis research aims to further understanding in the field of continuous authentication using behavioural biometrics. We are contributing a novel dataset that encompasses the gesture data of 15 users playing Minecraft with a Samsung Tablet, each for a duration of15 minutes. Utilizing this dataset, we employed machine learning (ML) binary classifiers, being Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Classifier (SVC), to determine the authenticity ofspecific user actions. Our most robust model was SVC, which achieved an average accuracy of approximately 90%, demonstrating that touch dynamics can effectively distinguish users. However, further studies are needed to make it viable option for authentication systems. You can access our dataset at the following link: [https://github.com/AuthenTech2023/authentech-repo](https://github.com/AuthenTech2023/authentech-repo)  \nKEYWORDS  \nContinuous Authentication, Machine Learning, Minecraft, Novel Dataset, Touch Gestures  \n1. INTRODUCTION  \nThe current authentication methods, which are primarily implemented at entry points, can be problematic in numerous scenarios. Once a device is unlocked, it remains unlocked. The device can also be vulnerable to smudge attacks and keyloggers. In a world of ever-advancing technology, security of our devices is vital, and a proposed solution for authentication is the implementation of continuous authentication methods. Continuous authentication relies on the idea that a device could learn the user’s behavioural biometrics patterns and identify whether an impostor or the actual user is using the device. Within this paper, we investigate continuous authentication methods rooted in machine learning. Our research is grounded in touchscreen data acquired from 15 volunteers playing the popular game Minecraft, from which we extracted essential features. These features served as the foundation for training and assessing our machine learning models. The three types of machine learning models we used were: RF, KNN and SVC. In this paper, we make the following contributions:  \n1. We created a public touch dynamics dataset that records the actions of 15 users who played Minecraft on an Android device. The dataset is available at: [https://github.com/AuthenTech2023/authentech-repo](https://github.com/AuthenTech2023/authentech-repo).  \n2. We trained and tested three models: KNN, SVC and RF. We then compared our outcome to results from previous works.  \nThis paper is organized as follows: Section 2 reviews the related literature on this field and highlights the research gap. Section 3 describes the methodology, including the data collection and the models employed. Section 4 presents the results. Section 5 discusses the findings and compares them with existing studies. Section 6 acknowledges the limitations of the research and suggests directions for future work. Section 7 concludes the paper and summarizes the main contributions.  \n2. LITERATURE REVIEW  \nUser authentication through touchscreen gestures has gained significant attention in recent years, fueled by the proliferation of smartphones and the need for robust security measures. The existing body of literature offers valuable insights into various models and methodologies employed to authenticate users based on their touch behavior. However, a notable gap in the literature lies in the","cbCaip2wT1m2SWpZ","https://ap.wps.com/l/cbCaip2wT1m2SWpZ","pdf",1455508,3,1,15,"English","en",105,"# Introduction\n# Literature Review\n# Methodology\n## Data Collection\n## Models\n# Results\n# Discussion\n# Limitations and Future Work\n# Conclusion","[{\"question\":\"What problem does continuous authentication address compared with entry-point authentication?\",\"answer\":\"It aims to verify the user continuously based on behavioral biometrics instead of leaving the device unlocked after the initial login.\"},{\"question\":\"What dataset is introduced in this research?\",\"answer\":\"A public touch-dynamics dataset containing gesture data from 15 users who played Minecraft on a Samsung tablet for about 15 minutes each.\"},{\"question\":\"Which machine learning model performed best and what accuracy was reported?\",\"answer\":\"Support Vector Classifier (SVC) performed best, achieving an average accuracy of approximately 90% in distinguishing user actions.\"}]","YOUR DEVICE MAY KNOW YOU BETTER THAN YOU KNOW YOURSELF - 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