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Implicit continuous User Authentication for Mobile Devices based on Deep Reinforcement Learning

Computer systems science & Engineering 2023年第2期44卷 1357-1372页

作者： Christy James Jose M.s.Rajasree Government Engineering College Idukki685603India APJ Abdul Kalam Technological University Thiruvananthapuram695016India

The predominant method for smart phone accessing is conﬁned to methods directing the authentication by means of Point-of-Entry that heavily depend on physiological biometrics like,ﬁngerprint or face.Implicit continuous authentication initiating to be loftier to conventional authentication mechanisms by continuously conﬁrming users’identities on continuing basis and mark the instant at which an illegitimate hacker grasps dominance of the session.However,divergent issues remain unaddressed.This research aims to investigate the power of Deep Reinforcement Learning technique to implicit continuous authentication for mobile devices using a method called,Gaussian Weighted Cauchy Kriging-based continuous czekanowski’s(GWCK-CC).First,a Gaussian Weighted Non-local Mean Filter Preprocessing model is applied for reducing the noise pre-sent in the raw input face images.Cauchy Kriging Regression function is employed to reduce the dimensionality.Finally,continuous czekanowski’s Clas-siﬁcation is utilized for proﬁcient classiﬁcation between the genuine user and attacker.By this way,the proposed GWCK-CC method achieves accurate authen-tication with minimum error rate and time.Experimental assessment of the pro-posed GWCK-CC method and existing methods are carried out with different factors by using UMDAA-02 Face Dataset.The results conﬁrm that the proposed GWCK-CC method enhances authentication accuracy,by 9%,reduces the authen-tication time,and error rate by 44%,and 43%as compared to the existing methods.

关键词： Deep reinforcement learning gaussian weighted non-local meanﬁlter cauchy kriging regression continuous czekanowski’s implicit continuous authentication mobile devices

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