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In the current intranet environment, information is becoming more readily accessed and replicated across a wide range of interconnected systems. Anyone using the intranet computer may access content that he does not have permission to access. For an insider attacker, it is relatively easy to steal a colleague’s password or use an unattended computer to launch an attack. A common one-time user authentication method may not work in this situation. In this paper, we propose a user authentication method based on mouse biobehavioral characteristics and deep learning, which can accurately and efficiently perform continuous identity authentication on current computer users, thus to address insider threats. We used an open-source dataset with ten users to carry out experiments, and the experimental results demonstrated the effectiveness of the approach. This approach can complete a user authentication task approximately every 7 seconds, with a false acceptance rate of 2.94% and a false rejection rate of 2.28%.

Sensor drift is a common issue in E-Nose systems and various drift compensation methods have received fruitful results in recent years. Although the accuracy for recognizing diverse gases under drift conditions has been largely enhanced, few of these methods considered online processing scenarios. In this paper, we focus on building online drift compensation model by transforming two domain adaptation based methods into their online learning versions, which allow the recognition models to adapt to the changes of sensor responses in a time-efficient manner without losing the high accuracy. Experimental results using three different settings confirm that the proposed methods save large processing time when compared with their offline versions, and outperform other drift compensation methods in recognition accuracy.