In order to solve the problem that the fuzziness and uncertainty of real data may seriously affect the classification results of few-shot learning, a Fuzzy Prototype Network (FPN) based on fuzzy reasoning was proposed by improving and optimizing the traditional few-shot learning prototype network. Firstly, the image feature information was obtained from Convolutional Neural Network (CNN) and fuzzy neural network, respectively. Then, linear knowledge fusion was performed on the two obtained parts of information to obtain the final image features. Finally, to achieve the final classification effect, the Euclidean distance between each category prototype and the query set was measured. A series of experiments were carried out on the mainstream datasets Omniglot and miniImageNet for few-shot learning classification. On miniImageNet dataset, the model achieves accuracy of 49.38% under the experimental setting of 5-way 1-shot, accuracy of 67.84% under the experimental setting of 5-way 5-shot, and accuracy of 51.40% under the experimental setting of 30-way 1-shot; and compared with the traditional prototype network, the model also has the accuracy greatly improved on Omniglot dataset.

Semi-supervised learning can effectively improve the generalization performance of algorithm by combining a few labeled samples and large number of unlabeled samples. The traditional semi-supervised Support Vector Machine (SVM) algorithm introduces unlabeled sample dependencies into the objective function to drive the decision-making surface through the low-density region, but it often brings problems such as high computational complexity and local optimal solution. At the same time, semi-supervised K-means algorithm faces the problems of how to effectively use the supervised information to initialize and update the centroid. To solve these problems, a novel learning algorithm of Semi-supervised K-means Assisted SVM (SKAS) was proposed. Firstly, an improved semi-supervised K-means algorithm was proposed, which was improved from two aspects:distance measurement and centroid iteration. Then, a fusion algorithm was designed to combine semi-supervised K-means algorithm with SVM in order to further improve the performance of the algorithm. The experimental results on six UCI datasets show that, the proposed method outperforms the current advanced semi-supervised SVM and semi-supervised K-means algorithms on five datasets and has the highest average accuracy.

In order to enhance the defensive ability and prediction ability of mobile network,a method for constructing mobile botnet based on a URL Shortening Services Flux (USSes-Flux) and Google Cloud Messaging for Android (GCM) was proposed. The mobile botnet model was designed with hybrid topology of central structure and peer-to-peer (P2P), USSes-Flux algorithm was presented, which increased robustness and stealthiness of Command and Control (C&C) channel. The control model was discussed. The states change of different bot, command design and propagation algorithm were also analyzed. In the test environment, the relationship between probability of short URL invalidness and number of required short URL was discussed. The static analysis, dynamic analysis and power testing of the mobile botnet and the samples of different C&C channel were carried out. The results show that the proposed mobile botnet is more stealthy, robust and low-cost.

Considering the low accuracy of the existing image segmentation method based on affinity propagation clustering, a FCAP algorithm which combined fuzzy connectedness and affinity propagation clustering was proposed. A Whole Fuzzy Connectedness (WFC) algorithm was also proposed with concerning the shortcoming of traditional fuzzy connectedness algorithms that can not get fuzzy connectedness of every pair of pixels. In FCAP, the image was segmented by using super pixel technique. These super pixels could be considered as data points and their fuzzy connectedness could be computed by WFC. Affinities between super pixels could be calculated based on their fuzzy connectedness and spatial distances. Finally, affinity propagation clustering algorithm was used to complete the segmentation. The experimental results show that FCAP is much better than the methods which use affinity propagation clustering directly after getting super pixels, and can achieve competitive performance when comparing with other unsupervised segmentation methods.

In IP network, tomography method can perform fault diagnosis by analyzing the end-to-end properties with low costs. However, most existing tomography based techniques have the following problems: 1) the end-to-end detected number is not sufficient to determine the state of each link; 2) as the scale of the network goes up, the diagnosis time may become unacceptable. To address these problems, a new congested link diagnosis algorithm based on Bayesian model was proposed in this paper. This method firstly modeled the problem as a Bayesian network, and then simplified the network by two steps and limited the number of multiple congested links. Therefore, the proposed method could greatly reduce the computational complexity and guarantee the diagnostic accuracy. Compared with the existing diagnosis algorithm which is called Clink, the proposed algorithm has higher diagnostic accuracy and shorter diagnosis time.