Aiming at the accuracy and error correction of cloud security situation prediction,a cloud security situation prediction method based on grey wolf optimization(GWO)and back propagation(BP)neural network is proposed.Firstly,the adaptive disturbance convergence factor is used to improve the GWO algorithm,so as to improve the convergence speed and accuracy of the algorithm.The Chebyshev chaotic mapping is introduced into the position update formula of GWO algorithm,which is used to select the features of the cloud security situation prediction data and optimize the parameters of the BP neural network prediction model to minimize the prediction output error.Then,the initial weights and thresholds of BP neural network are modified by the improved GWO algorithm to increase the learning efficiency and accuracy of BP neural network.Finally,the real data sets of Tencent cloud platform are predicted.The simulation results show that the proposed method has lower mean square error(MSE)and mean absolute error(MAE)compared with BP neural network,BP neural network based on genetic algorithm(GA-BP),BP neural network based on particle swarm optimization(PSO-BP)and BP neural network based on GWO algorithm(GWO-BP).The proposed method has better stability,robustness and prediction accuracy.
Combining the principle of antibody concentration with the idea of biological evolution, this paper proposes an adaptive target detection algorithm for cloud service security based on Bio-Inspired Performance Evaluation Process Algebra(Bio-PEPA). The formal modelling of cloud services is formally modded by Bio-PEPA and the modules are transformed between cloud service internal structures and various components. Then, the security adaptive target detection algorithm of cloud service is divided into two processes, the short-term optimal action selection process which selects the current optimal detective action through the iterative operation of the expected function and the adaptive function, and the long-term detective strategy realized through the updates and eliminations of action planning table. The combination of the two processes reflects the self-adaptability of cloud service system to target detection. The simulating test detects three different kinds of security risks and then analyzes the relationship between the numbers of components with time in the service process. The performance of this method is compared with random detection method and three anomaly detection methods by the cloud service detection experiment. The detection time of this method is 50.1% of three kinds of detection methods and 86.3% of the random detection method. The service success rate is about 15% higher than that of random detection methods. The experimental results show that the algorithm has good time performance and high detection hit rate.
In view of the privacy security issues such as location information leakage in the interaction process between the base station and the sensor nodes in the sensor-cloud system, a base station location privacy protection algorithm based on local differential privacy(LDP) is proposed. Firstly, through the local obfuscation algorithm(LOA), the base station can get the data of the real location and the pseudo location by flipping a coin, and then send the data to the fog layer, then the obfuscation location domain set is obtained. Secondly, in order to reconstruct the location distribution of the real location and the pseudo location in the base station, the location domain of the base station is divided into several decentralized sub-regions, and a privacy location reconstruction algorithm(PLRA) is performed in each sub-region. Finally, the base station correlates the location information of each sub-region, and then uploads the data information containing the disturbance location to the fog node layer. The simulation results show that compared with the existing base station location anonymity and security technique(BLAST) algorithm, the proposed method not only reduce the algorithm’s running time and network delay, but also improve the data availability. So the proposed method can protect the location privacy of the base station more safely and efficiently.
