Concerning the problem of convolutional neural network having poor classification performance to time-series remote sensing images with abnormal data, an end-to-end network based on the integration of multi-mode and multi-single-mode architecture was introduced. Firstly, multi-scale features of the multi-dimensional time-series were extracted by the multivariate time-series model and the univariate time-series model. Then, the spatio-temporal sequence feature construction was completed by automatic coding based on the pixel spatial coordinate information. Finally, the classification was implemented by fully connected layer and the softmax function. In the case of data anomaly (data loss and data distortion), the proposed algorithm was compared with commonly used time-series remote sensing image classification algorithms such as 1D Convolutional Neural Network (1D-CNN), Multi-Channels Deep Neural Network (MCDNN), Time Series Convolutional Neural Networks (TSCNN) and Long Short-Term Memory (LSTM) network. Experimental results showed that the proposed network using the end-to-end multi-mode and multi-single-mode architecture fusion had the highest classification accuracy in the case of data anomaly, and the F1 value reached 93.40%.

To defense application-layer Distributed Denial of Service (DDoS) built on the normal network layer, a defense model based on Web behavior trajectory in the Web application server was constructed. User's access behavior was abstracted into Web behavior trajectory, and according to the generation approach about attack request as well as behavior characteristics of user access to Web pages, four kinds of suspicion were defined, including access dependency suspicion, behavior rate suspicion, trajectory similarity suspicion, and trajectory deviation suspicion. The deviation values between normal sessions and attack sessions were calculated to detect the application-layer DDoS to a specific website. The defense model prohibited the user access from DDoS when detecting the attack request generated by the user. In the experiment, real data was acted as the training set. Then, through simulating different kinds of attack request, the defense model could identify the attack request and take the defense mechanism against the attack. The experimental results demonstrate that the model can detect and defense the application-layer DDoS to a specific website.

Wireless Sensor Network (WSN) adopts the default Distributed Address Assignment Mechanism (DAAM) of ZigBee technology to assign the addresses to the nodes without considering the optimization of the network topology, which causes the waste of network depth. In this paper, the authors proposed Distributed Borrowed Address Assignment (DBAA) algorithm to increase the success rate of joined nodes, which assigned the free addresses from 2-hops neighbors to the nodes for the optimization of the network topology. The theoretical analysis and simulation results show that DBAA algorithm outperforms both DAAM and Single Level Address Reorganization (SLAR) scheme in terms of the success rate of address assignment, communication overhead, and the average time of assigning addresses.