Aiming at the existing feature selection methods lacking the ability to measure the overlap/separation of different classes of data, an Interference Entropy of Two-Class Distinguishing (IET-CD) method was proposed to evaluate the two-class distinguishing ability of features. For the feature containing two classes (positive and negative), firstly, the mixed conditional probability of the negative class samples within the range of positive class data and the probability of the negative class samples belonging to the positive class were calculated; then, the confusion probability was calculated by the mixed conditional probability and attribution probability, and the confusion probability was used to calculate the positive interference entropy. In the similar way, the negative interference entropy was calculated. Finally, the sum of positive and negative interference entropies was taken as the two-class interference entropy of the feature. The interference entropy was used to evaluate the distinguishing ability of the feature to the two-class sample. The smaller the interference entropy value of the feature, the stronger the two-class distinguishing ability of the feature. On three UCI datasets and one simulated gene expression dataset, five optimal features were selected for each method, and the two-class distinguishing ability of the features were compared, so as to compare the performance of the methods. The experimental results show that the proposed method is equivalent or better than the NEFS （Neighborhood Entropy Feature Selection） method, and compared with the Single-indexed Neighborhood Entropy Feature Selection (SNEFS), feature selection based on Max-Relevance and Min-Redundancy (MRMR), Joint Mutual Information (JMI) and Relief method, the proposed method is better in most cases. The IET-CD method can effectively select features with better two-class distinguishing ability.

In order to solve the problem that Scale Invariant Feature Transform (SIFT) algorithm has low matching accuracy and long time consuming in image matching, an improved image matching algorithm based on grid motion statistical feature, namely RANSAC-GMS, was proposed. Firstly, the image was pre-matched by Oriented FAST and Rotated BRIEF (ORB) algorithm and Grid-based Motion Statistics (GMS) was used to support the estimator to distinguish the correct matching points from the wrong matching points. Then, an improved RANdom SAmple Consensus (RANSAC) algorithm was used to filter the feature points according to the distance similarity between the matching points, and an evaluation function was used to reorganize the filtered new datasets to eliminate the mismatching points. The experiments were carried out on Oxford standard image library and images taken in reality. Experimental results show that the average matching accuracy of the proposed algorithm in image matching is over 91%. Compared with algorithms such as GMS, SIFT and ORB, the near-scene matching accuracy and the far-scene matching accuracy of the proposed algorithm are improved by 16.15 percentage points and 3.56 percentage points respectively. The proposed algorithm can effectively eliminate mismatching points and achieve further improvement of image matching accuracy.

Aiming at the low performance of existing anomaly detection algorithms based on random forest, a random forest algorithm combining double features and relaxation boundary was proposed for anomaly detection. Firstly, in the process of constructing binary decision tree of random forest with normal class data only, the range of two features (each feature had a corresponding eigenvalue range) were recorded in each node of the binary decision tree, and the double-feature eigenvalue ranges were used as the basis for abnormal point judgment. Secondly, during the anomaly detection, if a sample did not satisfy the double-feature eigenvalue range in the decision tree node, the sample would be marked as a candidate exception class; otherwise, the sample would enter the lower nodes of the decision tree and continue the comparision with the corresponding double-feature eigenvalue range. The sample would be marked as candidate normal class if there were no lower nodes. Finally, the discriminative mechanism in random forest algorithm was used to distinguish the class of the samples. Experimented results on five UCI datasets show that the proposed method has better performance than the existing random forest algorithms for anomaly detection, and its comprehensive performance is equivalent to or better than isolation Forest (iForest) and One-Class SVM (OCSVM), and stable at a high level.

With the development of virtualization technology, co-resident attack becomes a common means to steal sensitive information from users. Aiming at the hysteresis of existing virtual machine dynamic migration method reacting to co-resident attacks, a virtual network function migration strategy based on security threat prediction in the context of 5G network slicing was proposed. Firstly, network slicing operation security was modeled based on Hidden Markov Model (HMM), and the network security threats were predicted by multi-source heterogeneous data. Then according to the security prediction results, the migration cost was minimized by adopting the corresponding virtual network function migration strategy. Simulation experimental results show that the proposed strategy can effectively predict the security threats and effectively reduce the migration overhead and information leakage time by using HMM, which has a better defense effect against co-resident attack.

Due to the weak development ability, Particle Swarm Optimization (PSO) algorithms have the shortages of low precision and slow convergence. Comparatively weak exploration ability of Differential Evolution (DE) algorithm, might further lead to a trap in the local extremum. A particle swarm-differential evolution fusion algorithm based on fuzzy Gaussian learning strategy was proposed. On the basis of the standard particle swarm algorithm, the elite particle population was selected, and the fusion mechanism of elite particle swarm-evolution was constructed by using mutation, crossover and selection evolution operators to improve particle diversity and convergence. A fuzzy Gaussian learning strategy according with human thinking characteristics was introduced to improve particle optimization ability, and further generate an elite particle swarm and differential evolution fusion algorithm based on fuzzy Gaussian learning strategy. Nine benchmark functions were calculated and analyzed in this thesis. The results show that the mean values of the functions Schwefel.1.2, Sphere, Ackley, Griewank and Quadric Noise are respectively 1.5E-39, 8.5E-82, 9.2E-13, 5.2E-17, 1.2E-18, close to the minimum values of the algorithm. The convergences of Rosenbrock, Rastrigin, Schwefel and Salomon functions are 1~3 orders of magnitude higher than those of four contrast particle swarm optimization algorithms. At the same time, the convergence of the proposed algorithm is 5%-30% higher than that of the contrast algorithms. The proposed algorithm has significant effects on improving convergence speed and precision, and has strong capabilities in escaping from the local extremum and global searching.

To describe the qualitative attributes in the regression model, it is usually necessary to introduce dummy variables. For the regression equation with dummy variables, a method was proposed to describe the different importance of the different dummy variables in the regression equation. The sums of square due to regression with dummy variables were descomposed, including the sum of the dummy variable part and that of non-dummy variable part, and the proportions of the two parts was calculated in the regression equation, and the proportion was taken as the index of relative importance of every dummy variable in regression equations. In sets of Lending Club and Prosper network with nearly 100 thousand lending data, the experimental results about the influence of the purpose of loan on the borrowing success rate and the influence of credit grade on the borrowing rate show that compared with the traditional regression equation which only provides a dummy variable coefficient and cannot shows its importance, the proposed method can show the importance of different dummy variables, and provide an important means to quantitatively analyze the influence degree of qualitative independent variables on the dependent variable in the regression equation.

Concerning the power complementary limitation due to the time-reversed assumption of prototype filter in the design of traditional DFT (Discrete Fourier Transform) modulated filter banks, a time domain modified method was introduced to design the DFT filter banks from the time domain perfect reconstruction perspectives in this paper. Moreover, the designed filter banks were applied to the filter banks based multi-carrier transceivers. The time domain modified method relaxed the time-reversed assumption of prototype filter, that is, the filter banks at the receiver were conjugate transpose form of the filter banks at the transmitter. Moreover, it adopted the time domain formula of the perfect reconstruction property as the solution to design the filter banks at the receiver, which would ensure the perfect reconstruction of filter banks and avoid the power complementary limitation in the design of prototype filter at the same time. Compared to the traditional design method, the time domain modified method improves the design freedom of prototype in the filter banks, so suitable prototype filters could be obtained according to the various application environments without considering power complementary restrictions. Moreover, the time domain modified DFT filter banks based multi-carrier transceivers has a better SER (Symbol Error Ratio) performance in QPSK (Quadrature Reference Phase Shift Keying) modulation, ideal and the 3GPP TS 25.104 pedestrian multipath channel and one-tap frequency-domain equalization.

Concerning the key problems of selecting threshold function in wavelet threshold denoising, in order to address the discontinuity of conventional threshold function and large deviation existing in the estimated wavelet coefficients, a continuous adaptive threshold function in the whole wavelet domain was proposed. It fully considered the characteristics of different sub-band coefficients in different scales, and set the energy of sub-band coefficients in different scales as threshold function's initial weights. Optimal weights were iteratively solved by using interval advanced-retreat method and golden section method, so as to adaptively improve approximation level between estimated and decomposed wavelet coefficients. The experimental results show that the proposed method can both efficiently reduce noise and simultaneously preserve the edges and details of image, also achieve higher Peak Signal-to-Noise Ratio (PSNR) under different noise standard deviations.

For Automated Trust Negotiation (ATN) consultative process may encounter the infinite cycling problem, the causes of the cycle were analyzed and the corresponding detection algorithm was designed to find and terminate the negotiation cycle. Interdependency relationships among policies in ATN were modeled as simple graph and the model's correctness was proved. The process of calculating simple grahp's reachability matrix was analyzed and cycle detection theorem was given. The algorithm of detecting cyclic policy interdependency was designed according to the theorem. Finally, a case study verifies the feasibility of the algorithm.

Based on the research of manual testing process, combined with the technology of UML and Expect program, an automated testing model applied to the function test of network protocol stack software in the kernel of the operating system was proposed. In Linux operating system, an automated test case using only one computer was exemplified to verify the feasibility of the model and its corresponding technology. This model settles the test problem of automated network configuration and data-driven, and at the same time reduces the requirements on hardware resources.