Challenge is the core element of digital games. The wide spectrum of physical, cognitive, and emotional challenge experiences provided by modern digital games can be evaluated subjectively using a questionnaire, the CORGIS, which allows for a post hoc evaluation of the overall experience that occurred during game play. Measuring this experience dynamically and objectively, however, would allow for a more holistic view of the moment-to-moment experiences of players. This study, therefore, explored the potential of detecting perceived challenge from physiological signals. For this, we collected physiological responses from 32 players who engaged in three typical game scenarios. Using perceived challenge ratings from players and extracted physiological features, we applied multiple machine learning methods and metrics to detect challenge experiences. Results show that most methods achieved a detection accuracy of around 80%. We discuss in-game challenge perception, challenge-related physiological indicators and AI-supported challenge detection to inform future work on challenge evaluation.

VPN has posed many difficulties for network security management. In this paper, we develop a robust method to classify the VPN traffic. Firstly, we investigate the VPN transmission process and find the turning packet interval (Named as TPI) is a valuable feature for VPN traffic classification. Then we employ the probability distribution of TPI to improve the robustness of classification process, which is named as TPIPD. Secondly, we evaluate our method using the ISCXVPN2016 dataset and find our method has higher classification accuracy compared with other related methods. We also find the distribution of the first few TPIs can be used to represent that of the entire TPIs of specific flow, thus our method can be used for online traffic classification. As TPIPD is a kind of probability feature, it is more robust than other traditional features. Finally, the experiments verify our methods can be used for mice flow identification.