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.

Nowadays, many malicious Web pages can launch the downloading of malware without any user interaction only by leveraging normal Web programming techniques and deceive victims into executing the downloaded malware. This type of attack is called auto-download. The existing defense mechanisms equipped with browsers can not effectively identify the attack. In order to solve the problem, an approach was presented to mitigate the attack. The downloading operations were monitored. When a download was performing, it would be checked to see whether it was triggered by the user interaction or not. Consequently, potential auto-download behaviors would be detected and terminated. The approach had been implemented in two browsers WebKitGtk+2.8.0 and Chromium 38.0.2113.1. Both of the two detection and defense systems were evaluated. The false negatives and false positives were 0, and performance overload was 1.26% and 7.79%. The experimental results show that the proposed approach can effectively detect and terminate the auto-download attack with less performance overload.