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A Novel Approach for Continuous Authentication of Mobile Users Using Reduce Feature Elimination (RFE): A Machine Learning Approach

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dc.contributor.author Kumari, Sonal
dc.contributor.author Singh, Karan
dc.contributor.author Khan, Tayyab
dc.contributor.author Ariffin, Mazeyanti Mohd
dc.contributor.author Mohan, Senthil Kumar
dc.contributor.author Baleanu, Dumitru
dc.contributor.author Ahmadian, Ali
dc.date.accessioned 2023-11-22T11:57:19Z
dc.date.available 2023-11-22T11:57:19Z
dc.date.issued 2023
dc.identifier.citation Kumari, Sonal...et.al. (2023). "A Novel Approach for Continuous Authentication of Mobile Users Using Reduce Feature Elimination (RFE): A Machine Learning Approach", Mobile Networks & Applications. tr_TR
dc.identifier.issn 1383-469X
dc.identifier.uri http://hdl.handle.net/20.500.12416/6571
dc.description.abstract Mobile phones are a valuable object in our daily life. With the acquisition of the latest technologies, their capabilities and demands increase day by day. However, acquiring the latest technologies makes mobile phones vulnerable to various security threats. Generally, people use passwords, pins, fingerprint locks, etc., to secure their mobile phones. Passwords and pins create so much burden for people always to remember their credentials. These traditional approaches are susceptible to brute force attacks, smudge attacks, and shoulder surfing attacks. Due to the difficulties mentioned above, researchers are leaning more towards continuous authentication. Therefore, this paper introduces an adaptive continuous authentication approach, a behavioral-based mobile authentication mechanism. In (Ehatisham-ul-Haq et al. J Netw Comput Appl 109:24-35, 2018), the authors achieved a good average accuracy of 97.95% with a Support vector machine classifier (SVM). We used LGB and RF and got 95.8% and 98.8% accuracy in user recognition. RF and LGB were trained for all five body positions separately to recognize each User among five users. This model also promises to reduce the system's cost and complexity by introducing the reduce feature elimination (RFE) technique during feature selection. RFE eliminates the less critical feature and reduces the dimension of the feature set. Hence, it demonstrates the benefits of our model for mobile authentication. tr_TR
dc.language.iso eng tr_TR
dc.relation.isversionof 10.1007/s11036-023-02103-z tr_TR
dc.rights info:eu-repo/semantics/closedAccess tr_TR
dc.subject Mobile tr_TR
dc.subject Continuous Authentication tr_TR
dc.subject Accuracy tr_TR
dc.subject Machine Learning tr_TR
dc.subject Feature Selection tr_TR
dc.subject Behavioral tr_TR
dc.title A Novel Approach for Continuous Authentication of Mobile Users Using Reduce Feature Elimination (RFE): A Machine Learning Approach tr_TR
dc.type article tr_TR
dc.relation.journal Mobile Networks & Applications tr_TR
dc.contributor.authorID 56389 tr_TR
dc.contributor.department Çankaya Üniversitesi, Fen - Edebiyat Fakültesi, Matematik Bölümü tr_TR


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