Network abnormal traffic detection is an important part of network security protection. At present， abnormal traffic detection methods based on deep learning treat the port number attribute the same as other traffic attributes， ignoring the importance of the port number. Considering the idea of attention， a novel abnormal traffic detection module based on Convolutional Neural Network （CNN） combining Port Attention Module （PAM） and Convolutional Block Attention Module （CBAM） was proposed to improve the performance of abnormal traffic detection. Firstly， the original network traffic was taken as the input of PAM， the port number attribute was separated and sent to the full connected layer， and the learned port attention weight value was obtained， and the traffic data after port attention was output by dot-multiplying with other traffic attributes. Then， the traffic data was converted into a grayscale map， and CNN and CBAM were used to extract the the channel and space information of the feature map more fully. Finally， the focus loss function was used to solve the problem of data imbalance. The proposed PAM has the advantages of few parameters， plug and play， and universal applicability. The accuracy of the proposed model is 99.18% for the binary-class classification task of abnormal traffic detection and 99.07% for the multi-class classification task on the CICIDS2017 dataset， and it also has a high recognition rate for classes with only a few training samples.

For insufficient edge weight window threshold design in Text Graph Convolutional Network （Text GCN）， to mine the word association structure more accurately and improve prediction accuracy， a fake review detection algorithm combining Gaussian Mixture Model （GMM） and Text GCN named F-Text GCN was proposed. The edge signal strength of fake reviews that are relatively weak compared to normal reviews in training data size was improved by using GMM nature to separate noise edge weight distributions. Additionally， considering the diversity of information sources， the adjacency matrix was constructed by combing documents， words， reviews and non-text features. Finally， the fake review association structure of the adjacency matrix was extracted through spectral decomposition of Text GCN. Validation experiments were performed on 126 086 actual Chinese reviews collected by a large domestic e-commerce platform. Experimental results show that， for detecting fake reviews， the F1 value of F-Text GCN is 82.92%， outperforming BERT （Bidirectional Encoder Representation from Transformers） and Text CNN by 10.46% and 11.60%， respectively， the F1 of F-Text GCN is 2.94% higher than that of Text GCN. For highly imitated fake reviews which are challenging to detect， F-Text GCN achieves the overall prediction accuracy of 94.71% by secondary detection on the samples that Support Vector Machine （SVM） was difficult to detect， which is 2.91% and 14.54% higher than those of Text GCN and SVM. Based on study findings， lexical interference in consumer decision-making is evident in fake reviews’ second-order graph neighbor structure. This result indicates that the proposed algorithm is especially suitable for extracting long-range word collocation structures and global sentence feature pattern variations for fake reviews detection.

IPv6 Neighbor Cache (NC) was very vulnerable to be attacked, therefore, an improved method named Reversed Detection Plus (RD+) was proposed. Timestamp and sequence were firstly introduced to limit strict time of response and response matching respectively; RD+ queue was defined to store timestamp and sequence, and Random Early Detection Based on Timestamp (RED-T) algorithm was designed to prevent Denial of Service (DoS) attacks. The experimental results show that RD+ can effectively protect IPv6 NC to resist spoofing and DoS attacks, and compared with Heuristic and Explicit (HE) and Secure Neighbor Discovery (SEND), RD+ has a low consumption of resources.

A new corner detection algorithm based on the autocorrelation matrix of Multi-channel Odd Gabor grAdient (MOGA) was proposed to suppress the decrease of corner positioning accuracy caused by the smoothed edge. The input image was transformed by 8-channel odd Gabor filter, and then autocorrelation matrices were constructed for each pixel by Gabor gradient correlation of the pixel and its surrounding pixels. If the sum of the normalized eigenvalues of the pixel was local maxima, the pixel was labeled as a corner. Compared with the classical algorithms, such as Harris and Curvature Scale Space (CSS), the proposed algorithm increased the average rate of correct detection by 17.74%, and decreased the average rate of positioning error by 18.15%. The experimental results show that the proposed algorithm has very good detection performance, and gets higher corner detection rate and better corner positioning accuracy.

In order to measure the fault tolerance ability of the parallel computers which take the k-ary n-cube as underlying topology, by constructing the minimum node set whose removal will lead to every k-ary 1-cube in the k-ary n-cube faulty, a recursive algorithm for finding the k-ary 1-cube subnet preclusion node cut of the k-ary n-cube was proposed. It is proved that at least kn-1 nodes need to be damaged if a rival wants to destroy all k-ary 1-cubes in the k-ary n-cube. The result indicates that there are still undamaged k-ary 1-cubes in the parallel computers which take the k-ary n-cube as underlying topology if the number of the faulty nodes does not exceed kn-1-1.

To solve the problem that the existing IP covert timing channel need to make agreed the encoding scheme between the information and IP packet timing interval, can’t dynamic adjustment of double-blind according to the network transmission quality in the process of sending and receiving, a double-blind dynamic adjustment strategy what is used to negotiate IP covert timing channel encoding scheme is proposed. By segmenting the network environment and depending on the dynamic network environment, the strategy selected the default encoding scheme, achieved double-blind dynamic adjustment of encoding scheme between sender and receiver. In order to verify the reliability of strategy, the experimental environment of covert timing channel is constructed. The results show that the proposed method can achieve double-blind dynamic adjustment between sender and receiver.

Based on the analysis of the traditional Canny algorithm, the adaptive filter took the place of the original Gaussian filter and made use of cross-entropy to measure the differences between the background and objectives. Combining Bayesian judgment theory, the average cross-entropy of posterior probability of the pixels of original image to objective and background areas presented differences between classes, and this paper maximized the posterior probability to judge pixels in which different regions to obtain the optimal level of the threshold. The experimental results show the improved algorithm has great edge detection effect.

Starting from the enhanced ability of fractional differential to image details, the authors analyzed the mechanism of fractional differential. By averaging the nonzero weights of operator template to the image pixels which have the same distance to constant coefficient “1” as well as utilizing self-dependency of surrounding pixels, an improved fractional order differential operator template was achieved. The experimental results show: in response to those images that have rich textural detail information, fractional differential outperforms integral differential operation to extract the textural detail information in smooth region witout too much gray scale change.

A new and simple iris location method was proposed. The algorithm includes the location of inside and outside of the iris. Based on the rough process of the iris image, for the inside location the author takes the method of the projection to the coordinate, and for the outside the author looks for the point which the content of information is least on the scanning line. For the distribution of the yawp for example eyelash, the point which we choose is not only one. The author abandons the wrong point using the corresponding templates. It is proved that the algorithm is feasible through experiments.

To extract skeleton based on ultraviolet rock fracture image using digital image processing technique, it needs to pretreat the rock fracture image first by image processing operation such as noise filtering, image segmentation, cavity filling, spur removal etc. Then an algorithm based on the structural elements of the layers thinning was proposed on the basis of the analysis of the skeleton characteristic and the skeleton of extraction algorithm. This algorithm can extract rock fractures of the skeleton very well. The experimental results show that the algorithm can extract a better skeleton of rock fracture efficiently and steadily.