The existing Generative Adversarial Network (GAN) synthesized face detection method has misjudgment of real faces with angles and occlusion, therefor a GAN-synthesized face detection method based on Deep Alignment Network (DAN) was proposed. Firstly, a facial landmark extraction network was designed based on DAN to extract the locations of facial landmarks of genuinus and synthesized faces. Then, in order to reduce the redundant information and feature dimensionality, each group of landmarks was mapped to the three-dimensional space by using the Principal Component Analysis (PCA) method. Finally, the features were classified by using 5-fold cross-validation of Support Vector Machine and the accuracy was calculated. Experimental results show that the proposed method improves the face dissonance caused by location errors by improving the accuracy of facial landmark location, which reduces the misjudgment rate of real faces. Compared with VGG19, XceptionNet and Dlib-SVM methods, this proposed method has the Area Under Receiver Operating Characteristic curve (AUC) increased by 4.48 to 32.96 percentage points and Average Precision (AP) increased by 4.26 to 33.12 percentage points on frontal faces; and has the AUC increased by 10.56 to 30.75 percentage points and AP increased by 7.42 to 42.45 percentage points on faces with angles and occlusion.

Aiming at the performance degradation of transmission performance of Transmission Control Protocol (TCP) in wireless network caused by the loss packet retransmission mechanism triggered by packet loss, an Adaptive transmission mechanism based on Forward Error Correction (AdaptiveFEC) was proposed. In the mechanism, the transmission performance of TCP was improved by the avoidance of triggering TCP loss packet retransmission mechanism, which realized by reducing data segment loss with forward error correction. Firstly, the optimal redundant segment ratio in current time was selected according to the current network status and the data transmission characteristics of the current connection. Then, the network status was estimated by analyzing the data segment sequence number in the TCP data segment, so that the redundant segment ratio was dynamically updated according to the network. Large number of experiment results show that, in the transmission environment with a round-trip delay of 20 ms and a packet loss rate of 5%, AdaptiveFEC can increase the transmission rate of TCP connection by 42% averagely compared to static forward error correction mechanism, and the download speed can be twice as much as the original speed with the proposed mechanism applied to file download applications.

In the enterprise network, if the internal attacker obtains the user's identity authentication information, his behavior will be very difficult to distinguish with the normal user. The current research on the abnormal user detection method in enterprise network is relatively simple and the detection rate is low. The user's authentication activity information directly reflects the user's interaction with various resources or personnel in the network. Based on this, a new abnormal user detection method by using user authentication activity information was proposed. The user's authentication activity was used to generate the user authentication graph, and then the attributes in the authentication graph were extracted based on the graph analysis method, such as the size of the largest connected components of the graph and the number of isolated certificates. These attributes reflect the user's authentication behavioral characteristics in the enterprise network. Finally, a supervised Support Vector Machine (SVM) was used to model the extracted graph attributes to indirectly identify and detect abnormal users in the network. After extracting the user graph vector, the training set and the test set, the penalty parameter and the kernel function were analyzed by taking different values. Through the adjustment of these parameters, the recall, accuracy and F1-Score of the propsed method have reached more than 80%. The experimental results show that the proposed method can effectively detect abnormal users in the enterprise network.