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目前,信息安全问题已经成为现代社会中数据分析的主要问题。为了能够及时且有效地应对各种网络威胁和攻击,网络管理人员要能够高效且准确地对整个网络的状态和发展趋势进行感知,从而合理配置网络资源,所以网络安全态势感知已经成为网络信息安全研究中的重点。数据作为网络安全态势感知的主要内容,只有通过有效的数据融合技术,提炼并且压缩安全信息数据,才能够为安全态势及威胁评估提供依据。但是网络设施不断高速建设和发展的背景下,安全态势数据源也呈现出来海量、多源、异构的特点,这对数据融合提出了更高的要求。本文通过层次化网络安全态势感知数据融合模型的创建,对网络流量数据特征及决策层融合,使用政务云处理大量数据,提高数据融合效率。
At present, the problem of information security has become the main problem of data analysis in modern society. In order to timely and effectively deal with various network threats and attacks, network managers must be able to effectively and accurately perceive the status and development trend of the entire network, so as to reasonably configure network resources. Therefore, the network security posture awareness has become a network information security The focus of the research. As the main content of network security situational awareness, data can be extracted and compressed only through effective data fusion techniques to provide a basis for the assessment of the security situation and threats. However, under the background of rapid construction and development of network facilities, the data sources of security situation also show massive, multi-source and heterogeneous features, which puts forward higher requirements for data fusion. In this paper, through the creation of hierarchical network security situational awareness data fusion model, the paper integrates the network traffic data features and decision-making layers, and uses government cloud to process large amount of data to improve data fusion efficiency.