In this paper,we propose an authentication method that use mouse and keystroke dynamics to enhance online privacy and security.The proposed method identifies personalized repeated user interface(UI)sequences by analyzi...In this paper,we propose an authentication method that use mouse and keystroke dynamics to enhance online privacy and security.The proposed method identifies personalized repeated user interface(UI)sequences by analyzing mouse and keyboard data.To this end,an Apriori algorithm based on the keystroke-level model(KLM)of the human–computer interface domain was used.The proposed system can detect repeated UI sequences based on KLM for authentication in the software.The effectiveness of the proposed method is verified through access test-ing using commercial applications that require intensive UI interactions.The results show using our cognitive mouse-and-keystroke dynamics system can com-plement authentication at the application level.展开更多
With the rapid development of information technology,information system security and insider threat detection have become important topics for organizational management.In the current network environment,user behavior...With the rapid development of information technology,information system security and insider threat detection have become important topics for organizational management.In the current network environment,user behavioral bio-data presents the characteristics of nonlinearity and temporal sequence.Most of the existing research on authentication based on user behavioral biometrics adopts the method of manual feature extraction.They do not adequately capture the nonlinear and time-sequential dependencies of behavioral bio-data,and also do not adequately reflect the personalized usage characteristics of users,leading to bottlenecks in the performance of the authentication algorithm.In order to solve the above problems,this paper proposes a Temporal Convolutional Network method based on an Efficient Channel Attention mechanism(ECA-TCN)to extract user mouse dynamics features and constructs an one-class Support Vector Machine(OCSVM)for each user for authentication.Experimental results show that compared with four existing deep learning algorithms,the method retains more adequate key information and improves the classification performance of the neural network.In the final authentication,the Area Under the Curve(AUC)can reach 96%.展开更多
基金supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF),funded by the Ministry of Education(2021R1I1A3058103,2019R1A2C1002525).
文摘In this paper,we propose an authentication method that use mouse and keystroke dynamics to enhance online privacy and security.The proposed method identifies personalized repeated user interface(UI)sequences by analyzing mouse and keyboard data.To this end,an Apriori algorithm based on the keystroke-level model(KLM)of the human–computer interface domain was used.The proposed system can detect repeated UI sequences based on KLM for authentication in the software.The effectiveness of the proposed method is verified through access test-ing using commercial applications that require intensive UI interactions.The results show using our cognitive mouse-and-keystroke dynamics system can com-plement authentication at the application level.
基金supported by the National Natural Science Foundation of China(61962015)the Guangxi Key Laboratory of Cryptography and Information Security Research Project,China(GCIS202127)+2 种基金the Central Guidance on Local Science and Technology Development Fund of Guangxi Province,China(ZY23055008)the Scientific Research and Technological Development Planning Project of Guilin,China(20220124-12)the Innovation Project of Guangxi Graduate Education,China(2023YCXS043).
文摘With the rapid development of information technology,information system security and insider threat detection have become important topics for organizational management.In the current network environment,user behavioral bio-data presents the characteristics of nonlinearity and temporal sequence.Most of the existing research on authentication based on user behavioral biometrics adopts the method of manual feature extraction.They do not adequately capture the nonlinear and time-sequential dependencies of behavioral bio-data,and also do not adequately reflect the personalized usage characteristics of users,leading to bottlenecks in the performance of the authentication algorithm.In order to solve the above problems,this paper proposes a Temporal Convolutional Network method based on an Efficient Channel Attention mechanism(ECA-TCN)to extract user mouse dynamics features and constructs an one-class Support Vector Machine(OCSVM)for each user for authentication.Experimental results show that compared with four existing deep learning algorithms,the method retains more adequate key information and improves the classification performance of the neural network.In the final authentication,the Area Under the Curve(AUC)can reach 96%.
