摘要
文中基于大数据技术,研究了基于支持向量机的网络流量分析与异常检测方法。首先,对网络流量数据进行预处理,如清洗、集成和转换等,以获取适合支持向量机分析的特征向量表示。然后,应用支持向量机分析技术对网络流量进行异常检测,通过构建超平面实现对正常样本和异常样本的分类。最后,利用NSL-KDD数据集进行实验验证,并评估该方法在网络流量异常检测中的性能。实验结果表明,基于支持向量机的网络流量异常检测方法在NSL-KDD数据集上取得了较好的准确率、召回率和精确率。
Based on big data technology,this paper studies the network traffic analysis and anomaly detection method based on support vector machine.First,the network traffic data is preprocessed,such as cleaning,integration and transformation,to obtain the eigenvector representation suitable for support vector machine analysis.Then,the support vector machine analysis technology is applied to network traffic anomaly detection,and the classification of normal samples and abnormal samples is realized by constructing a hyperplane.Finally,the NSL-KDD dataset is used for experimental verification,and the performance of the method in network traffic anomaly detection is evaluated.The experimental results show that the network traffic anomaly detection method based on support vector machine has achieved good accuracy,recall rate and precision on the NSL-KDD dataset.
作者
王长青
WANG Changqing(Zhengzhou Technician College,Zhengzhou 450000,China)
出处
《移动信息》
2023年第12期192-193,206,共3页
MOBILE INFORMATION
关键词
大数据分析
网络流量
支持向量机
预处理
Big data analysis
Network traffic
Support vector machine
Preprocessing
