摘要
网络异常行为识别是保障网络安全的重要方法。文章提出了一种结合长短时记忆网络(Long Short-Term Memory,LSTM)和贝叶斯分类器的网络流量分析方法,旨在通过自动提取网络流量的时间序列特征来提升异常流量识别的准确性。具体而言,首先利用LSTM模型从网络流量中提取隐藏特征表示,随后结合贝叶斯分类器进行流量分类,最后采用NSL-KDD数据集对所提方法进行测试,并通过多项评估。实验结果表明,该方法在识别不同类型流量时均表现出良好的效果,验证了其在网络异常检测领域的实用性和鲁棒性。
Identifying abnormal network behavior is an important method for ensuring network security.The article proposes a network traffic analysis method that combines Long Short-Term Memory(LSTM)and Bayesian classifier,aiming to improve the accuracy of abnormal traffic recognition by automatically extracting time series features of network traffic.Specifically,the LSTM model is first used to extract hidden feature representations from network traffic,followed by traffic classification using a Bayesian classifier.Finally,the proposed method is tested using the NSL-KDD dataset and evaluated through multiple criteria.The experimental results show that the method performs well in identifying different types of traffic,verifying its practicality and robustness in the field of network anomaly detection.
作者
肖博元
XIAO Boyuan(Nanning Rail Transit Co.,Ltd.,Nanning 530000,China)
关键词
人工智能
网络安全
流量分析
贝叶斯分类器
artificial intelligence,network security
traffic analysis
bayesian classifier
作者简介
肖博元(1986-),本科,工程师,研究方向:网络安全、人工智能。
