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.

Since most malwares are designed using decentralized architecture to resist detection and countering, in order to fast and accurately detect Peer-to-Peer (P2P) bots at the stealthy stage and minimize their destructiveness, a real-time detection system for stealthy P2P bots based on statistical features was proposed. Firstly, all the P2P hosts inside a monitored network were detected using means of machine learning algorithm based on three P2P statistical features. Secondly, P2P bots were discriminated based on two P2P bots statistical features. The experimental results show that the proposed system is able to detect stealthy P2P bots with an accuracy of 99.7% and a false alarm rate below 0.3% within 5 minutes. Compared to the existing detection methods, this system requires less statistical characteristics and smaller time window, and has the ability of real-time detection.