基于特征分析的网络安全防护策略研究

Research on network security protection strategies based on feature analysis

随着互联网的迅速发展,网络安全问题日益严峻,传统的防护手段已无法应对网络数据爆炸式增长的新挑战。针对这一现状,文章提出了一种基于大数据特征学习的网络安全防护策略。为提高对网络威胁的检测识别能力,该策略通过构建特征分析模型完成了对大量异构网络数据的关联,实现了对网络数据的采集、存储、分析和响应等全流程自动化处理,并通过实验初步验证了其有效性。在未来的工作中,将进一步优化特征分析模型,并在实际大规模网络环境中进行验证,以建立一个满足大数据时代需求的智能化安全防护体系。

With the rapid development of Internet,the problem of network security is becoming increasingly serious,and the traditional means of protection can no longer meet the new challenges of explosive growth of network data.In response to this situation,this article proposes a network security protection strategy based on big data feature learning.To improve the detection and recognition ability of network threats,this strategy completes the association of a large amount of heterogeneous network data by constructing a feature analysis model,realizing the full process automation processing of network data collection,storage,analysis,and response.The effectiveness has been preliminarily verified through experiments.In future work,the feature analysis model will be further optimized and validated in actual large-scale network environments to establish an intelligent security protection system that meets the needs of the big data era.