The sources of digital resources in the distribution network mainly include three aspects: equipment ontology, production operation, and management. The data is described in various forms and stored in various types o...
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In order to solve the resource management challenge of cloud service center when the user demand peaks periodically, reduce resource waste and energy consumption, improve resource utilization and system performance, a...
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ISBN:
(数字)9798331532093
ISBN:
(纸本)9798331532109
In order to solve the resource management challenge of cloud service center when the user demand peaks periodically, reduce resource waste and energy consumption, improve resource utilization and system performance, a scheduling algorithm based on multi-objective optimization is proposed in this paper. Firstly, this paper analyzes the main problems of current cloud computing resource scheduling, constructs an adaptive virtual resource scheduling framework, establishes a multi-objective scheduling optimization model, and ensures the balance of resource allocation by quantifying task levels and establishing a “task-virtual node” allocation model. To solve the model, a load-balancing strategy based on improved particle swarm optimization (PSO) was proposed to improve resource utilization and reduce energy consumption. Other scheduling factors that need to be considered in the actual scheduling environment, including computing cost, load, task completion time, and convergence speed, are further studied and improved in the experiment. Based on the above scheduling factors, multi-objective optimization of cloud computing resource scheduling is carried out to achieve the optimal comprehensive performance of cloud computing task scheduling. Finally, through simulation experiments, it is verified that the algorithm has better performance in terms of cost, load, task completion time, convergence speed, and CPU consumption, especially when the task volume increases; it can balance the load and reduce energy consumption more effectively.
To obtain carbon peaking and carbon neutrality goals, it is necessary to establish a national carbon emission platform for monitoring and reporting carbon dioxide equivalent. Energy data contains a good deal of import...
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In order to ensure the trusted access of power collection terminals in a disaster recovery environment, this paper proposes a cross-domain authentication scheme based on blockchain. Firstly, an improved PBFT algorithm...
ISBN:
(纸本)9798400716362
In order to ensure the trusted access of power collection terminals in a disaster recovery environment, this paper proposes a cross-domain authentication scheme based on blockchain. Firstly, an improved PBFT algorithm based on reputation value election is designed, which increases the calculation of node reputation value and improves the dynamic addition and removal of consensus nodes and the election of master nodes based on node reputation value and failure conditions. Then, a cross-domain authentication model for power collection terminals based on blockchain is constructed. The blockchain certificate format and cross-domain authentication protocol are designed, and the security and efficiency are analyzed. The analysis results show that the scheme has security properties such as resistance to distributed attacks, and the communication overhead is lower than that of the traditional PBFT algorithm.
The current power data centers face the risk of data leakage during the data exchange process. Given the increasing scale and complexity of power data centers, traditional methods for identifying anomalies in the temp...
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The rapid proliferation of electric vehicle(EV)charging infrastructure introduces critical cybersecurity vulnerabilities to powergrids *** study presents an innovative anomaly detection framework for EV charging stat...
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The rapid proliferation of electric vehicle(EV)charging infrastructure introduces critical cybersecurity vulnerabilities to powergrids *** study presents an innovative anomaly detection framework for EV charging stations,addressing the unique challenges posed by third-party aggregation *** approach integrates node equations-based on the parameter identification with a novel deep learning model,xDeepCIN,to detect abnormal data reporting indicative of aggregation *** employ a graph-theoretic approach to model EV charging networks and utilize Markov Chain Monte Carlo techniques for accurate parameter *** xDeepCIN model,incorporating a Compressed Interaction Network,has the ability to capture complex feature interactions in sparse,high-dimensional charging *** results on both proprietary and public datasets demonstrate significant improvements in anomaly detection performance,with F1-scores increasing by up to 32.3%for specific anomaly types compared to traditional methods,such as wide&deep and DeepFM(Factorization-Machine).Our framework exhibits robust scalability,effectively handling networks ranging from 8 to 85 charging ***,we achieve real-time monitoring capabilities,with parameter identification completing within seconds for networks up to 1000 *** research contributes to enhancing the security and reliability of renewable energy systems against evolving cyber threats,offering a comprehensive solution for safeguarding the rapidly expanding EV charging infrastructure.
With the large-scale deployment of loT devices, loT security faces huge threats from zero-day attacks, and there is an urgent need to conduct research on loT zero-day attack detection technologies. Aiming at the probl...
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ISBN:
(数字)9798350388343
ISBN:
(纸本)9798350388350
With the large-scale deployment of loT devices, loT security faces huge threats from zero-day attacks, and there is an urgent need to conduct research on loT zero-day attack detection technologies. Aiming at the problem of lack of available samples for zero-day attack detection in loT, we propose a zero-day attack detection method based on generative zero-shot learning. Feature extraction is performed based on the LightGBM model, and then multi-layer perceptron is used to generate attribute vectors. We generate pseudo feature space of data category based on conditional variational autoencoders to train zero-day attack detectors. To comply with real attack detection scenarios, we adopt the generalized zero-shot learning method to conduct verification on the UNSW-NB15 data set. Experiment results show that the precision, recall and Fl of most attack types are above 90%. The average accuracy of our method in detecting zero-day attacks is 93.6%, which is much higher than the baseline method and achieve great detection results.
Energy data, especially power data, has the advantages of covering a wide range of industries, high value density, and good accuracy, relying on energy big data can widely describe all kinds of terminal production and...
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ISBN:
(数字)9798350366099
ISBN:
(纸本)9798350366105
Energy data, especially power data, has the advantages of covering a wide range of industries, high value density, and good accuracy, relying on energy big data can widely describe all kinds of terminal production and life activities. However, the privacy computing platforms of various vendors often adopt different technical architectures and algorithm protocols in the early implementation process, which makes the implementation of various platforms have great differences, resulting in the direct interconnection between heterogeneous privacy computing platforms of various organizations. Aiming at the interoperability needs and problems of energy big data privacy computing platforms with different technical architectures, this paper decouple the management plane and data plane of the privacy computing platform, and in accordance with the principle of business priority, proposes a new privacy computing interconnection model of “unified business collaboration component + flexible configuration algorithm engine” suitable for energy big data. Through the design of the east-west and North-South interface framework of the privacy computing platform and the security access principle of the algorithm engine, the whole process control, cross-platform interconnection, and internal and external network penetration modeling and analysis of energy big data collaboration with the outside world are realized, which meets the key data security protection requirements of the stategrid such as distributed computing and storage of data inside and outside the platform. It is of great significance to promote energy big data to empower external institutions such as governments, banks and operators.
In this paper, we study the consistency verification technology for multi-party collaborative privacy-preserving operations of energy big data, and propose a consistency assessment algorithm based on minimum hash sign...
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ISBN:
(数字)9798350386776
ISBN:
(纸本)9798350386783
In this paper, we study the consistency verification technology for multi-party collaborative privacy-preserving operations of energy big data, and propose a consistency assessment algorithm based on minimum hash signatures for the privacy and consistency challenges in energy big data processing. The algorithm realizes the assessment of the consistency of the target dataset through data compression and signature design of the data source, avoids direct access to the original data, and guarantees data privacy and consistency of processing results. The experimental results show that the algorithm is able to effectively assess data consistency on energy big datasets such as electricity and gas consumption, and the results are close to the exact algorithm with high credibility and efficiency.
Identifying hidden anomalous behavior is a major challenge in anomaly detection, particularly in complex systems where anomalies are buried within massive log data and cannot be easily identified through simple patter...
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