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.

Aiming at the problem of high energy consumption caused by the increase of base station density in heterogeneous dense network, an energy-efficient method for micro base station deployment in heterogeneous networks was proposed. Firstly, the feasibility of micro base station positions was considered to mitigate the effects of environmental conditions. Then the optimization target value was weighed under different user distribution probability to enhance adaptability for different user distribution scenarios. Finally, an energy-efficient deployment algorithm for micro base stations was proposed by jointly optimizing the number, deployment position and power configuration of micro base stations. Simulation results show that the proposed method improves energy efficiency by up to 26% compared with the scheme which only optimizes the number and location of micro base stations. The experimental results demonstrate that the combined optimization method can improve the energy efficiency of the system compared with the deployment method without considering the power factor, and verifies the influence of the micro base station power on the energy efficiency of heterogeneous network.

In order to deal with the cache pollution attacks in Content Centric Networking (CCN), a defense scheme based on cache diversification was proposed. To reduce the attack scope, the in-network content services were divided into three categories and different cache strategies were used for different services. For private and real-time services, contents were directly delivered without being cached; for streaming media services, contents were pushed to be cached in the edge of network according to probablity; for document services, the priority was caching contents in the upstream, then pushing them to the downstream. Then different defense methods were configured on different nodes. For the edge nodes, attacks were detected by observing the request probability variation of different contents; for the upstream nodes, contents with low request rate were ruled out from the cache space by setting filter rules. The simulation results show that the network average hit ratio under service diversification mechanism is 17.3% higher than that under CCN with traditional caching strategies.The proposed scheme can effectively improve the defense capability of the network for the cache pollution attack.

The relatively large time cost of the existing influence algorithms does not fit the social networks of which the scale keeps expanding. An influence optimization model was proposed based on the network community structure for solving problem of large time cost. Firstly evaluate nodes influence in each community and dig core members, and then find a small subset of nodes in the set composed of the core nodes and linking community nodes to get the maximization diffusion with minimization cost. The experimental results demonstrate that our model achieves the subset with more abroad influence diffusion and reduces running time compared with traditional methods. Its influence coverage is up to 90%.