The identification of key nodes in complex network plays an important role in the optimization of network structure and effective propagation of information. Local structural Entropy （LE） can be used to identify key nodes by using the influence of the local network on the whole network instead of the influence of nodes on the whole network. However， the cases of the highly aggregative network and nodes forming a loop with neighbor nodes are not considered in LE， which leads to some limitations. To address these limitations， firstly， an improved LE based node importance evaluation method， namely PLE （Penalized Local structural Entropy）， was proposed， in which based on the LE， the Clustering Coefficient （CC） was introduced as a penalty term to penalize the highly aggregative nodes in the network appropriately. Secondly， due to the fact that the penalty of PLE penalizing the nodes in triadic closure structure is too much， an improved method of PLE， namely PLEA （Penalized Local structural Entropy Advancement） was proposed， in which control coefficient was introduced in front of the penalty term to control the penalty strength. Selective attack experiments on five real networks with different sizes were conducted. Experimental results show that in the western US states grid and the US Airlines， PLEA has the identification accuracy improved by 26.3% and 3.2% compared with LE respectively， by 380% and 5.43% compared with K-Shell （KS） method respectively， and by 14.4% and 24% compared with DCL （Degree and Clustering coefficient and Location） method respectively. The key nodes identified by PLEA can cause more damage to the network， verifying the rationality of introducing the CC as a penalty term， and the effectiveness and superiority of PLEA. The integration of the number of neighbors and the local network structure of nodes with the simplicity of computation makes it more effective in describing the reliability and invulnerability of large-scale networks.

In intelligent computing services， data analysis and processing are provided for the service consumer by the service provider through Internet， and a learning model is established to complete intelligent computing function. Due to the lack of effective communication channels between service providers and service consumers， as well as the fuzzy and messy requirement descriptions of the service consumer feedback， there is a lack of a unified service requirement acquisition method to effectively analyze， organize and regulate the continuously changing requirement of users， which leads to the failure of intelligent computing services to make a rapid improvement according to the user’s requirements. Aiming at the problems of continuity and uncertainty of requirement changes in service development， a requirement acquisition method for intelligent computing services was proposed. The application feedback and questions of intelligent computing services were firstly obtained from Stack Overflow question and answer forum. Then， the knowledge classification and prioritization were performed on them by using different learning models （including Support Vector Machine （SVM）， naive Bayes and TextCNN） according to the types of requirements concerned by the service consumer. Finally， a customized service requirement template was used to describe the requirements of intelligent computing services.

Concerning the problem that the current research on password semantics is mainly based on English datasets and restricted to some units like common words or surnames, by using data analysis technology based on password strings, a Chinese context password analysis method based on known-password elements was proposed with the pattern library based on Chinese poems and idioms in Chinese context. Firstly, the known-password element was identified. Then, it was considered as a single password degree of freedom. Finally, the freedom attack cost within a given attack success rate was calculated and the quantitative security of password was obtained. After quantitative analysis of large amounts of plaintext passwords by designed experiments, it is concluded that 80% of user passwords are low secure and can be easily broken by dictionary attacks in Chinese context.

Internet traffic classification is a process of identifying network applications and classifying corresponding traffic, which is considered as the most basic function of modern network management and security system. And application-related traffic classification is the basic technology of recent network security. Traditional traffic classification methods include port-based prediction methods and payload-based depth detection methods. In current network environment, there are some practical problems in traditional methods, such as dynamic ports and encryption applications. Therefore, Machine Learning (ML) technology based on traffic statistics is used to classify and identify traffic. Machine learning can realize centralized automatic search by using provided traffic data and describe useful structural patterns, which is helpful to intelligently classify traffic. Initially, Naive Bayes method was used to identify and classify network traffic classification, performing well on specific flows with accuracy over 90%, while on traffic such as peer-to-peer transmission network traffic (P2P) with accuracy only about 50%. Then, methods such as Support Vector Machine (SVM) and Neural Network (NN) were used, and neural network method could make accuracy of overall network classification reach 80% or more. A number of studies show that the use of a variety of machine learning methods and their improvements can improve the accuracy of traffic classification.

Aiming at the problem that the lip feature dimension is too high and sensitive to the scale space, a technique based on the Scale-Invariant Feature Transform (SIFT) algorithm was proposed to carry out the speaker authentication. Firstly, a simple video frame image neat algorithm was proposed to adjust the length of the lip video to the same length, and the representative lip motion pictures were extracted. Then, a new algorithm based on key points of SIFT was proposed to extract the texture and motion features. After the integration of Principal Component Analysis (PCA) algorithm, the typical lip motion features were obtained for authentication. Finally, a simple classification algorithm was presented according to the obtained features. The experimental results show that compared to the common Local Binary Pattern (LBP) feature and the Histogram of Oriental Gradient (HOG) feature, the False Acceptance Rate (FAR) and False Rejection Rate (FRR) of the proposed feature extraction algorithm are better, which proves that the whole speaker lip motion recognition algorithm is effective and can get the ideal results.

In the multi-user chaotic communication system, the increase in the number of communication users as well as the quasi-orthogonality between chaotic sequences cause channel interference. Therefore, the Bit Error Rate (BER) of multi-user chaotic communication system, in which different chaotic signals used Schmidt orthogonalization method to produce the corresponding orthogonal chaotic sequences as spreading code, was studied in Additive White Gaussian Noise (AWGN) and Rayleigh fading channel. Firstly, the statistical autocorrelation and cross-correlation characteristics of each orthogonal chaotic sequence were taken as an important index to measure the performance of orthogonal chaotic spreading code sequence, and the statistical correlated characteristics of each orthogonal chaotic sequence and both the mean and variance of the cross-correlation curve were tested and analyzed. Secondly, the BER of each orthogonal chaotic spreading code was obtained by two channel simulation experiments, and the intrinsic correlation found by contrasting the BER of orthogonal chaotic spreading code and its corresponding statistical correlated characteristics was revealed at that time. Lastly, the BERs of multi-user chaotic communication system of two different channels were compared and analyzed and the effect on BER caused by different channels was revealed. The simulation results show that the space-time orthogonal chaotic sequence of phase space chaotic signals can obtain low BER in the two channels, and especially, space-time orthogonal chaotic sequence can obtain lower BER in Rayleigh fading channel, and also show that the proposed method can effectively reduce the interference between the channels in the multi-user communication and is more conducive to the needs of multi-user communication.

Considering that the traditional method of histogram equalization can make the image produce unnatural enhanced results, a new color image contrast enhancement approach was proposed, which equalized color components by dependently using co-occurrence matrix. Firstly, gray correlative characteristics of pixels were combined in 3×3 neighborhoods to construct a co-occurrence matrix. Secondly, the method of co-occurrence histogram equalization was applied to luminance component only and the chrominance components were preserved. Finally, dark channel prior was used to solve the problem mentioned above to obtain the ideal image. Comparing the proposed enhanced algorithm with the other three typical enhancement algorithms, it is shown that the proposed algorithm not only considers the whole and local image information, but also deals with the histogram spikes and halo effect. The experimental results demonstrate that the proposed method can enhance the color images effectively.

To improve the security and reliability of image transmission, an image encryption algorithm based on combination Low Density Parity Check (LDPC) coding with chaotic system was proposed. Firstly, the algorithm used parity encoding to extend pixels' value of image into 10 bits and calculated its deviation acted as chaotic initial value. Secondly, Arnold transformation was used to scramble the positions of image pixels and Henon mapping was used to diffuse the values of pixels. Finally, the high 2 bits were separated from 10 bits of pixel value and transmitted faultlessly by LDPC code, the other 8 bits acted as the encryption result. The experimental results show that the proposed algorithm has strong sensitivity to the keys and plaintext, possesses favorable avalanche effect, and it can resist plaintext attack and differential attack effectively. Moreover, the encryption result has strong ability of resisting cutting and noise attacks.

Through analyzing the characteristics of the edge sets of the optimal solutions from Traveling Salesmen Problem (TSP), a kind of new model was proposed to produce the referring optimization edge sets for Lin-Kernighan algorithm on the basis of authors' previous research (WANG DONG, WU XIANG-BIN. Strategy for improving the performance of chained Lin-Kernighan algorithm. Journal of Computer Applications, 2007,27(11): 2826-2829). The number in the edge sets produced by the new model is less than those produced by normal algorithms or previous research. Meanwhile, the new edge sets include more edges that belong to the global optimal solution than them. Applying the new model to Lin-Kernighan algorithm, the execution time of the algorithm is further reduced, without losing the algorithm accuracy for a single call. Furthermore, the solution performance of Lin-Kernighan algorithm is improved also. With previous research achievement, the performance of all hybrid algorithms using Lin-Kernighan algorithm as the local search algorithm could be improved too.

To improve the security of image sharing and encryption, an algorithm which combined the scrambling encryption with sharing technology and pixel diffusion was proposed. Firstly, the Logistic chaotic mapping algorithm was used to generate the parameter of Arnold transform. Secondly, two-dimensional Arnold transform with variable parameters was adopted to scramble pixel positions of the image. Finally, three-dimensional Arnold transform with variable parameters was adopted to diffuse pixel values of the scrambled image, so the image could be decomposed into two images. The experimental results show that the algorithm has a strong sensitive effect on the external keys, resists plaintext attack and differential attack effectively, and possesses favorable avalanche effect. Moreover, there is a close relationship between the internal keys and the original plaintext image.

Rdundancy technology is very important to improve the reliability of system.A kind of new method of dual-redundancy flight control computer system was presented in detail.First,the system architecture and the basic principle working were introduced in brief.Second,how to write multiple-task arithmetic in the embedded real-time operating system of VxWorks was introduced.This dual-redundancy system can improve safety reliability and task reliability effectively,and by using this method,the designed system becomes good real-time and practicable.