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The fact that the security facilities within a system are closely coupled and the security facilities between systems are unconnected results in an isolated protection structure for systems, and gives rise to a serious challenge to system security integrations and system controls. Also, the need for diversified services and flexible extensions of network security asks for more considerations and contributions from the perspective of software engineering in the process of designing and constructing security systems. Based on the essence of the virtualization technique and the idea of software-defined networks, we in this paper propose a novel software-defined security architecture for systems. By abstracting the traditional security facilities and techniques, the proposed security architecture provides a new, simple, effective, and programmable framework in which security operations and security controls can be decoupled, and thereby reduces the software module sizes, decreases the intensity of software developments, and improves the security extensibility of systems.

In view of the existing user similarity calculation principle of recommendation algorithm is single, and recommender system accuracy is not well, we propose a novel social multi-attribute collaborative filtering algorithm (SoMu). We first define the user attraction similarity by users’ historical rated behaviors using graph theory, and secondly, define the user interaction similarity by users’ social friendship which is based on the social relationship of being followed and following. Then, we combine the user attraction similarity and the user interaction similarity to obtain a multi-attribute comprehensive user similarity model. Finally, realize personalized recommendation according to the comprehensive similarity model. Experimental results on Douban and MovieLens show that the proposed algorithm successfully incorporates multiple attributes in social networks to recommendation algorithm, and improves the accuracy of recommender system with the improved comprehensive similarity computing model.

With the development of online social networks, they rapidly become an ideal platform for information about social information diffusion, commodity marketing, shopping recommendation, opinion expression and social consensus. The social network information propagation has become a research hotspot correspondingly. Meanwhile, information diffusion contains complex dynamic genesis in online social networks. In view of the diversity of information transmission, the efficiency of propagation and the convenience of interaction, it is very important to regulate the accuracy, strengthen the public opinion monitoring and formulating the information control strategy. The purpose of this study is to quantify the intensity of the influence, especially provides a theoretical basis for studying the state transition of different user groups in the evolution process. As existing epidemic model paid less attention to influence factors and previous research about influence calculation mainly focused on static network topology but ignored individual behavior characteristics, we propose an information diffusion dynamics model based on dynamic user behaviors and influence. Firstly, according to the multiple linear regression model, we put forward a method to analyze internal and external factors for influence formation from two aspects:personal memory and user interaction. Secondly, for a similar propagation mechanism of information diffusion and epidemics spreading, in this paper we present an improved SIR model based on mean-field theory by introducing influence factor. The contribution of this paper can be summarized as follows. 1) For the influence quantification, different from the current research work that mainly focuses on network structure, we integrate the internal factors and external factors, and propose a user influence evaluation method based on the multiple linear regression model. The individual memory principle is analyzed by combining user attributes and individual behavior. User interaction is also studied by using the shortest path method in graph theory. 2) On modeling the information diffusion, by referring SIR model, we introduce the user influence factor as the parameter of the state change into the epidemic model. The mean-field theory is used to establish the differential equations. Subsequently, the novel information diffusion dynamics model and verification method are proposed. The method avoids the randomness of the artificial setting parameters within the model, and reveals the nature of multi-factors coupling in the information transmission. Experimental results show that the optimized model can comprehend the principle and information diffusion mechanism of social influence from a more macroscopic level. The study can not only explain the internal and external dynamics genesis of information diffusion, but also explore the behavioral characteristics and behavior laws of human. In addition, we try to provide theoretical basis for situation awareness and control strategy of social information diffusion.

Combining the advantages of chaotic coupled system and conventional one-way hash algorithm, a new keyed one-way hash function was presented. In the proposed approach, chaotic coupled system was made by Tent map and Logistic chaotic map, which produced chaotic data as dynamic parameters that replaced the fixed parameters of conventional hash function. The experimental results show that this method has large key space, strong one-way property, and sensitivity to the initial conditions and keys.