云计算和分布式系统发展迅猛,云端的数据安全变得日趋重要。文中针对现有云安全框架进行了分析,并通过对底层云数据隐私安全的相关技术进行了比较,在Cloud Accountability Life Cycle模型的基础上提出了可信任云框架;同时面对云端数据...云计算和分布式系统发展迅猛,云端的数据安全变得日趋重要。文中针对现有云安全框架进行了分析,并通过对底层云数据隐私安全的相关技术进行了比较,在Cloud Accountability Life Cycle模型的基础上提出了可信任云框架;同时面对云端数据可能产生泄露的潜在威胁,指出在保证云安全的前提下,对云端可追溯数据进行审计是一种非常有效的方法,并提出对系统内核日志的采集与分析,是确保可追溯的一种可信且稳定的云安全审计模式。最后通过对比不同基于检测控制的数据安全审计机制,分析各自的优势与存在的问题,并针对其中的不足进行了有效改进,对实现基于日志分析的云安全审计模式提供了可行性认证。展开更多
In order to improve the performance of the attribute reduction algorithm to deal with the noisy and uncertain large data, a novel co-evolutionary cloud-based attribute ensemble multi-agent reduction（CCAEMR） algorith...In order to improve the performance of the attribute reduction algorithm to deal with the noisy and uncertain large data, a novel co-evolutionary cloud-based attribute ensemble multi-agent reduction（CCAEMR） algorithm is proposed.First, a co-evolutionary cloud framework is designed under the M apReduce mechanism to divide the entire population into different co-evolutionary subpopulations with a self-adaptive scale. Meanwhile, these subpopulations will share their rewards to accelerate attribute reduction implementation.Secondly, a multi-agent ensemble strategy of co-evolutionary elitist optimization is constructed to ensure that subpopulations can exploit any correlation and interdependency between interacting attribute subsets with reinforcing noise tolerance.Hence, these agents are kept within the stable elitist region to achieve the optimal profit. The experimental results show that the proposed CCAEMR algorithm has better efficiency and feasibility to solve large-scale and uncertain dataset problems with complex noise.展开更多
文摘云计算和分布式系统发展迅猛,云端的数据安全变得日趋重要。文中针对现有云安全框架进行了分析,并通过对底层云数据隐私安全的相关技术进行了比较,在Cloud Accountability Life Cycle模型的基础上提出了可信任云框架;同时面对云端数据可能产生泄露的潜在威胁,指出在保证云安全的前提下,对云端可追溯数据进行审计是一种非常有效的方法,并提出对系统内核日志的采集与分析,是确保可追溯的一种可信且稳定的云安全审计模式。最后通过对比不同基于检测控制的数据安全审计机制,分析各自的优势与存在的问题,并针对其中的不足进行了有效改进,对实现基于日志分析的云安全审计模式提供了可行性认证。
基金The National Natural Science Foundation of China(No.61300167)the Open Project Program of State Key Laboratory for Novel Software Technology of Nanjing University(No.KFKT2015B17)+3 种基金the Natural Science Foundation of Jiangsu Province(No.BK20151274)Qing Lan Project of Jiangsu Provincethe Open Project Program of Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education(No.JYB201606)the Program for Special Talent in Six Fields of Jiangsu Province(No.XYDXXJS-048)
文摘In order to improve the performance of the attribute reduction algorithm to deal with the noisy and uncertain large data, a novel co-evolutionary cloud-based attribute ensemble multi-agent reduction（CCAEMR） algorithm is proposed.First, a co-evolutionary cloud framework is designed under the M apReduce mechanism to divide the entire population into different co-evolutionary subpopulations with a self-adaptive scale. Meanwhile, these subpopulations will share their rewards to accelerate attribute reduction implementation.Secondly, a multi-agent ensemble strategy of co-evolutionary elitist optimization is constructed to ensure that subpopulations can exploit any correlation and interdependency between interacting attribute subsets with reinforcing noise tolerance.Hence, these agents are kept within the stable elitist region to achieve the optimal profit. The experimental results show that the proposed CCAEMR algorithm has better efficiency and feasibility to solve large-scale and uncertain dataset problems with complex noise.
