Combining user behavioural information at the feature level to enhance continuous authentication systems
作者：
Highlights：
• Anomaly detection to solve continuous authentication.
• New method for combining behavioural information at the feature level.
• Behavioural data represented using a novel SAX based on Random Trees Embedding.
• DNA sequence alignment techniques to compare behavioural dynamics.
• Density-based clustering algorithms to extract behavioural cores.
摘要
•Anomaly detection to solve continuous authentication.•New method for combining behavioural information at the feature level.•Behavioural data represented using a novel SAX based on Random Trees Embedding.•DNA sequence alignment techniques to compare behavioural dynamics.•Density-based clustering algorithms to extract behavioural cores.
论文关键词：Anomaly detection,Behavioural information combination,Continuous authentication,User and Entity Behaviour Analytics
论文评审过程：Received 10 December 2021, Revised 9 February 2022, Accepted 3 March 2022, Available online 15 March 2022, Version of Record 25 March 2022.
论文官网地址：https://doi.org/10.1016/j.knosys.2022.108544
