摘要
为保证云计算环境下网络数据传输过程中数据的保密性、完整性以及流畅性,需要对云计算环境安全框架进行研究,目前的云计算环境安全框架系统设计方法主要是利用恩尼格码加密技术和分割二进制码技术实现当前云计算环境下网络数据的安全传输与通信;存在网络节点能量开销较大,且数据安全性判断平均准确率较低的问题;为提高云计算环境下网络数据的安全性判断准确率,避免网络节点的能量浪费,提出一种基于LabWindows的云计算环境安全框架系统设计方法,首先运用LabWindows对云计算环境下的数据进行采集,然后利用证据信任度求取算法对云计算环境下的网络数据安全性进行判断;其次将异常漂移检测器与恶意节点ID号过滤器有机结合,剔除云计算环境中的恶意攻击数据;再利用数字证书对云计算环境下的客户端与服务器进行身份认证;最后利用LabWindows平台创建云计算环境安全框架模型;实验结果证明,利用该方法能够节省云计算环境下网络节点的能量开销,对网络数据安全性判断准确率较高。
In order to ensure that cloud computing environment in the process of the network data transmission data confidentiality, in tegrity and fluency, need security framework for research on cloud computing environment, the current cloud computing environment security framework system design method is mainly use the Enigma code encryption technology and segmentation binary code technology to realize the current cloud computing environment the security of the network data transmission and communication. The problem is that the network node has a high energy cost and the average accuracy of the data security is low. In order to enhance the security of network data in cloud computing environment judgment accuracy, avoid network node energy waste, presents a cloud computing environment based on LabWin dows security framework system design method, first using LabWindows to cloud computing environment data acquisition, and then use evi dence credibility calculating algorithm in cloud computing environment of the network data security for judgment ; The second is to combine the anomalous drift detector with the malicious node--Id filter, which removes the malicious attack data from the cloud computing environ ment; Using digital certificates to authenticate clients and servers in the cloud computing environment; Finally, use the LabWindows plat form to create a cloud computing environment security framework model. The experimental results show that the nlethud can save the energy expenditure of network nodes in cloud computing environment, and the accuracy of network data security is higher.
出处
《计算机测量与控制》
2018年第2期142-145,149,共5页
Computer Measurement &Control
基金
广东省大数据分析与处理重点实验室开放基金项目资助(2017013)
广东教育学会"十三五"教育科研重点课题(GDESH13010)