Biblio

With the development of artificial intelligence, facial expression recognition (FER) has greatly improved performance in deep learning, but there is still a lot of room for improvement in the study of combining attention to focus the network on key parts of the face. For facial expression recognition, this paper designs a network model, which use spatial transformer network to transform the input image firstly, and then adding channel attention and spatial attention to the convolutional network. In addition, in this paper, the GELU activation function is used in the convolutional network, which improves the recognition rate of facial expressions to a certain extent.

In the application scenarios of cloud computing, big data, and mobile Internet, covert and diverse network attacks have become a serious problem that threatens the security of enterprises and personal information assets. Abnormal network behaviour detection based on network behaviour characteristics has become an important means to protect network security. However, existing frameworks do not make full use of the characteristics of the correlation between continuous network behaviours, and do not use an algorithm that can process time-series data or process the original feature set into time-series data to match the algorithm. This paper proposes a time-series abnormal network behaviour detection framework. The framework consists of two parts: an algorithm model (DBN-BiGRU) that combines Deep Belief Network (DBN) and Bidirectional Gated Recurrent Unit (BiGRU), and a pre-processing scheme that processes the original feature analysis files of CICIDS2017 to good time-series data. This detection framework uses past and future behaviour information to determine current behaviours, which can improve accuracy, and can adapt to the large amount of existing network traffic and high-dimensional characteristics. Finally, this paper completes the training of the algorithm model and gets the test results. Experimental results show that the prediction accuracy of this framework is as high as 99.82%, which is better than the traditional frameworks that do not use time-series information.

We provide a generic framework that, with the help of a preprocessing phase that is independent of the inputs of the users, allows an arbitrary number of users to securely outsource a computation to two non-colluding external servers. Our approach is shown to be provably secure in an adversarial model where one of the servers may arbitrarily deviate from the protocol specification, as well as employ an arbitrary number of dummy users. We use these techniques to implement a secure recommender system based on collaborative filtering that becomes more secure, and significantly more efficient than previously known implementations of such systems, when the preprocessing efforts are excluded. We suggest different alternatives for preprocessing, and discuss their merits and demerits.