基于大数据的网络流量异常检测应用研究  

Application Research on Anomaly Detection of Network Traffic Based on Big Data

作　　者：刘晶晶 Liu Jingjing(Lanzhou Modern Vocational College,Lanzhou 730300,China)

机构地区：[1]兰州现代职业学院,兰州730300

出　　处：《黑龙江科学》2024年第24期74-76,共3页Heilongjiang Science

摘　　要：网络流量检测是识别网络环境中潜在恶意活动的关键环节。在海量网络流量数据中高效地甄别出异常流量是网络安全领域的研究核心,对于保障网络安全、优化网络运维至关重要。提出一种基于大数据分析技术的网络流量异常检测模型,通过大数据分析技术有效解决网络流量动态变化带来的问题,显著提升异常检测的准确性,有效降低误报率,为网络安全维护提供新的解决方案。Network traffic detection is a key step to identify potential malicious activities in the network environment.Efficient identification of abnormal traffic from massive network traffic data is the core of network security research.It is crucial for ensuring network security and optimizing network operation and maintenance.The study proposes a network traffic anomaly detection model based on big data analysis technology.Through big data analysis technology,we can effectively solve the problems caused by dynamic changes in network traffic,significantly improve the accuracy of anomaly detection,effectively reduce the false positive rate,and provide a new solution for network security maintenance.

关 键 词：大数据应用 异常检测 网络流量 网络安全 

分 类 号：TP393.08[自动化与计算机技术—计算机应用技术] TP311.13[自动化与计算机技术—计算机科学与技术]