Dispersed computing is a new resourcecentric computing paradigm.Due to its high degree of openness and decentralization,it is vulnerable to attacks,and security issues have become an important challenge hindering its ...Dispersed computing is a new resourcecentric computing paradigm.Due to its high degree of openness and decentralization,it is vulnerable to attacks,and security issues have become an important challenge hindering its development.The trust evaluation technology is of great significance to the reliable operation and security assurance of dispersed computing networks.In this paper,a dynamic Bayesian-based comprehensive trust evaluation model is proposed for dispersed computing environment.Specifically,in the calculation of direct trust,a logarithmic decay function and a sliding window are introduced to improve the timeliness.In the calculation of indirect trust,a random screening method based on sine function is designed,which excludes malicious nodes providing false reports and multiple malicious nodes colluding attacks.Finally,the comprehensive trust value is dynamically updated based on historical interactions,current interactions and momentary changes.Simulation experiments are introduced to verify the performance of the model.Compared with existing model,the proposed trust evaluation model performs better in terms of the detection rate of malicious nodes,the interaction success rate,and the computational cost.展开更多
以非侵入式负荷分解为基础,对用户异常用电行为进行研究。采用Kmeans聚类算法提取负荷状态特征;采用深度学习算法中的序列到序列翻译(sequence to sequence, seq2seq)模型,将电力用户用电总数据分解成单个电器的功耗数据;结合SVM算法对...以非侵入式负荷分解为基础,对用户异常用电行为进行研究。采用Kmeans聚类算法提取负荷状态特征;采用深度学习算法中的序列到序列翻译(sequence to sequence, seq2seq)模型,将电力用户用电总数据分解成单个电器的功耗数据;结合SVM算法对分解后多种家用电器用电数据进行异常检测。在UKDALE数据集实验结果表明,该模型不仅能提高分解准确度、降低分解误差,而且多个电器数据结合分析实现了用户异常行为检测。展开更多
Current trusted computing platform only verifies application＇s static Hash value, it could not prevent application from being dynamic attacked. This paper gives one static analysis-based behavior model building metho...Current trusted computing platform only verifies application＇s static Hash value, it could not prevent application from being dynamic attacked. This paper gives one static analysis-based behavior model building method for trusted computing dynamic verification, including control flow graph （CFG） building, finite state automata （FSA） constructing, e run cycle removing, e transition removing, deterministic finite state （DFA） constructing, trivial FSA removing, and global push down automata （PDA） constructing. According to experiment, this model built is a reduced model for dynamic verification and covers all possible paths, because it is based on binary file static analysis.展开更多
基金supported in part by the National Science Foundation Project of P.R.China (No.61931001)the Fundamental Research Funds for the Central Universities under Grant (No.FRFAT-19-010)the Scientific and Technological Innovation Foundation of Foshan,USTB (No.BK20AF003)。
文摘Dispersed computing is a new resourcecentric computing paradigm.Due to its high degree of openness and decentralization,it is vulnerable to attacks,and security issues have become an important challenge hindering its development.The trust evaluation technology is of great significance to the reliable operation and security assurance of dispersed computing networks.In this paper,a dynamic Bayesian-based comprehensive trust evaluation model is proposed for dispersed computing environment.Specifically,in the calculation of direct trust,a logarithmic decay function and a sliding window are introduced to improve the timeliness.In the calculation of indirect trust,a random screening method based on sine function is designed,which excludes malicious nodes providing false reports and multiple malicious nodes colluding attacks.Finally,the comprehensive trust value is dynamically updated based on historical interactions,current interactions and momentary changes.Simulation experiments are introduced to verify the performance of the model.Compared with existing model,the proposed trust evaluation model performs better in terms of the detection rate of malicious nodes,the interaction success rate,and the computational cost.
文摘以非侵入式负荷分解为基础,对用户异常用电行为进行研究。采用Kmeans聚类算法提取负荷状态特征;采用深度学习算法中的序列到序列翻译(sequence to sequence, seq2seq)模型,将电力用户用电总数据分解成单个电器的功耗数据;结合SVM算法对分解后多种家用电器用电数据进行异常检测。在UKDALE数据集实验结果表明,该模型不仅能提高分解准确度、降低分解误差,而且多个电器数据结合分析实现了用户异常行为检测。
基金Supported by the National High Technology Research and Development Program of China (863 Program) (2006AA01Z442, 2007AA01Z411)the National Natural Science Foundation of China (60673071, 60970115)Open Foundation of State Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education in China (AISTC2008Q03)
文摘Current trusted computing platform only verifies application＇s static Hash value, it could not prevent application from being dynamic attacked. This paper gives one static analysis-based behavior model building method for trusted computing dynamic verification, including control flow graph （CFG） building, finite state automata （FSA） constructing, e run cycle removing, e transition removing, deterministic finite state （DFA） constructing, trivial FSA removing, and global push down automata （PDA） constructing. According to experiment, this model built is a reduced model for dynamic verification and covers all possible paths, because it is based on binary file static analysis.
基金Supported by the National Natural Science Foundation of China under Grant No.60303021(国家自然科学基金)the National HighTech Research and Development Plan of China under Grant No.2005AA118020(国家高技术研究发展计划(863))
