Bao Yi Wang

Bio: Bao Yi Wang is an academic researcher from North China Electric Power University. The author has contributed to research in topics: Cloud computing & Encryption. The author has an hindex of 2, co-authored 17 publications receiving 15 citations.

The Application of an Improved Integration Algorithm of Support Vector Machine to the Prediction of Network Security Situation

Abstract: In order to grasp the security situation of the network accurately and provide effective information for managers of network.GeesePSOSEN-SVM algorithm is proposed in this paper. It can produce and train multiple independent SVM through Bootstrap method and increase the degree of difference among SVM based on learning theories of negative correlation to construct the fitness function.GeesePSO algorithm is used to calculate the optimal weights of SVM.The algorithm chooses the high weights of SVM to integrate. At last, through the experiment on MATLAB for network security situational prediction,the results show that the absolute prediction error is smaller ,and the right trend rate is higher.

A Remote Data Integrity Verification Scheme Based on Cloud Computing

Abstract: Aiming at solving data integrity protection problems in the cloud , a remote data integrity verification scheme is proposed. Firstly, the data integrity verification is constructed based on homomorphic identification and data fragment structure. Secondly, by introducing random mask, the public verification is realized and by building index-hash table (IHT), the scheme can support dynamic verification. Finally, use the MapReduce for parallel computing, which reduces computation overhead side and storage overhead. The security and performance analyses show that our proposed scheme is secure and reliable.

Research on Algorithm of Distributed Reactive Power Optimization Based on Cloud Computing and Improved NSGA-II

Abstract: A distributed reactive power optimization algorithm is put forward based on cloud computing and improved NSGA-II (fast non-dominated sorting genetic algorithm) in this paper. It is designed to solve problem of multi-objective reactive power optimization with huge amounts of data in power grid, whose difficulties lie in local optimum and slow processing speed. First, NSGA-II's crossover and mutation operator are improved based on Cloud Model, so as to satisfy the adaptive characteristics. In this way, we improved global optimization ability and convergence speed when dealing with large-scale reactive power optimization. Second, we introduced cloud computing, parallelized the proposed algorithm based on MapReduce programming framework. In this way, we achieved distributed improved NSGA-II algorithm, effectively improved the calculation speed of handling massive high-dimensional reactive power optimization. Through theoretical study demonstrated the superiority of the algorithm to solve the Multi-Objective reactive power optimization.

An Improved Task and Role-Based Access Control Model with Multi-Constraint

Abstract: A combination of Task and Role-based Access Control with multi-constraint is put forward in this paper. It is designed to solve problem of access control management about collaborators in workflow system, whose difficulties lie in complex authorization and low users efficiency. It combines the tasks and roles, classifies tasks, simplifies permissions management, defines the mutually exclusive roles and binding tasks and formulates dynamic users allocation policies by establishing a users execution history table to improving the efficiency. Finally, a specific dynamic access control design is given for electric power enterprise equipment maintenance management workflow, the given example shows that the model and algorithm satisfies the principle of least permission and separation of duties and ensures the workflow system to execute tasks safely and efficiently.

A Short-Term Distributed Load Forecasting Algorithm Based on Spark and IPPSO_LSSVM

Abstract: To improve the accuracy of load forecasting and cope with the challenge of single computer’s insufficient computing resource, a short-term distributed load forecasting model based on LSSVM optimized by IPPSO is proposed. Uncertain parameters are optimized by improved parallel particle swarm algorithm which runs on the Spark on Yarn memory computing platform. The real load data provided by EUNITE is used, and experiments and analysis are conducted on an 8-node cloud computing platform. The results show that the accuracy of the algorithm proposed by our paper is better than the traditional functional networks algorithm, the efficiency of the algorithm is better than MR-OSELM-WA, and the algorithm has good ability of parallelization.

PORs: Proofs of Retrievability for Large Files

Abstract: In this paper, we define and explore proofs of retrievability (PORs). A POR scheme enables an archive or back-up service (prover) to produce a concise proof that a user (verifier) can retrieve a target file F, that is, that the archive retains and reliably transmits file data sufficient for the user to recover F in its entirety.A POR may be viewed as a kind of cryptographic proof of knowledge (POK), but one specially designed to handle a large file (or bitstring) F. We explore POR protocols here in which the communication costs, number of memory accesses for the prover, and storage requirements of the user (verifier) are small parameters essentially independent of the length of F. In addition to proposing new, practical POR constructions, we explore implementation considerations and optimizations that bear on previously explored, related schemes.In a POR, unlike a POK, neither the prover nor the verifier need actually have knowledge of F. PORs give rise to a new and unusual security definition whose formulation is another contribution of our work.We view PORs as an important tool for semi-trusted online archives. Existing cryptographic techniques help users ensure the privacy and integrity of files they retrieve. It is also natural, however, for users to want to verify that archives do not delete or modify files prior to retrieval. The goal of a POR is to accomplish these checks without users having to download the files themselves. A POR can also provide quality-of-service guarantees, i.e., show that a file is retrievable within a certain time bound.

Dynamic Audit Services for Outsourced Storages in Clouds

Abstract: In cloud, the security issue in outsourced storage data is the difficult challenge. To overcome the problem, traditional approach developed a dynamic audit service for verifying the integrity of an untrusted and outsourced storage. An audit service is constructed based on the techniques, fragment structure, random sampling, and index-hash table, supporting provable updates to outsourced data and timely anomaly detection. The method based on probabilistic query and periodic verification for improving the performance of audit services. The audit service is performed by TPA monitoring. Sometimes the TPA may have chances to hide anomaly details to cloud users. To overcome the drawback, propose a dynamic audit service in the cloud. By the method it can dynamically audit the anomaly and send intimation to cloud user. So that it can secure the cloud storage data

Forecasting of Energy-Related CO2 Emissions in China Based on GM(1,1) and Least Squares Support Vector Machine Optimized by Modified Shuffled Frog Leaping Algorithm for Sustainability

Abstract: Presently, China is the largest CO2 emitting country in the world, which accounts for 28% of the CO2 emissions globally. China’s CO2 emission reduction has a direct impact on global trends. Therefore, accurate forecasting of CO2 emissions is crucial to China’s emission reduction policy formulating and global action on climate change. In order to forecast the CO2 emissions in China accurately, considering population, the CO2 emission forecasting model using GM(1,1) (Grey Model) and least squares support vector machine (LSSVM) optimized by the modified shuffled frog leaping algorithm (MSFLA) (MSFLA-LSSVM) is put forward in this paper. First of all, considering population, per capita GDP, urbanization rate, industrial structure, energy consumption structure, energy intensity, total coal consumption, carbon emission intensity, total imports and exports and other influencing factors of CO2 emissions, the main driving factors are screened according to the sorting of grey correlation degrees to realize feature dimension reduction. Then, the GM(1,1) model is used to forecast the main influencing factors of CO2 emissions. Finally, taking the forecasting value of the CO2 emissions influencing factors as the model input, the MSFLA-LSSVM model is adopted to forecast the CO2 emissions in China from 2018 to 2025.

A New Speculative Execution Algorithm Based on C4.5 Decision Tree for Hadoop

Abstract: As a distributed computing platform, Hadoop provides an effective way to handle big data. In Hadoop, the completion time of job will be delayed by a straggler. Although the definitive cause of the straggler is hard to detect, speculative execution is usually used for dealing with this problem, by simply backing up those stragglers on alternative nodes. In this paper, we design a new Speculative Execution algorithm based on C4.5 Decision Tree, SECDT, for Hadoop. In SECDT, we speculate completion time of stragglers and also of backup tasks, based on a kind of decision tree method: C4.5 decision tree. After we speculate the completion time, we compare the completion time of stragglers and of the backup tasks, calculating their differential value, and selecting the straggler with the maximum differential value to start the backup task. Experiment result shows that the SECDT can predict execution time more accurately than other speculative execution methods, hence reduce the job completion time.