基于深度学习的网络Web异常流量检测方法  

Web Abnormal Traffic Detection Method Based on Deep Learning

作　　者：徐丞 XU Cheng(Chengdu Hongan Huayu Technology Co.,Ltd.,Chengdu 610000,China)

机构地区：[1]成都鸿安华宇科技有限公司,四川成都610000

出　　处：《微型电脑应用》2023年第12期151-154,158,共5页Microcomputer Applications

摘　　要：为了灵敏感知网络环境态势,提高用户隐私数据安全性,提出基于深度学习的网络Web异常流量检测方法。利用数据平面开发工具包设计Web流量采集架构,设置环境抽象层、内存管理层、调试组件等核心部件,实时捕获流量包;对流量数据数值化和归一化过程预处理,选用Daubechiesl小波函数对数据信号做离散小波变换,设定滑动窗口,分解数值序列,结合平均值、标准差和能量占比等特征参数,从数值序列中提取异常流量特征;利用长短时记忆网络学习特征向量,获得不同特征的时序关系,引入注意力机制对异常检测贡献度较高的特征赋权,构建多层感知机网络,输出最终检测结果。仿真实验表明,所提方法可同时满足高检测率和低虚警率的要求,为Web网络安全提供保障。ion layer,memory management layer,and debugging components to capture traffic packets in real time.The data signal is subjected to discrete wavelet transform,the sliding window is set,the numerical sequence is decomposed,and the characteristic parameters such as average value,standard deviation and energy ratio are combined to extract abnormal traffic characteristics from the numerical sequence;the long and short-term memory network is used to learn the feature vector to obtain different.The time series relationship of features is introduced,the attention mechanism is introduced to weight the features with high contribution to anomaly detection,and a multi-layer perceptron network is constructed to output the final detection result.Simulation experiments show that the proposed method can meet the requirements of high detection rate and low false alarm rate at the same time,and provide guarantee for Web network security.

关 键 词：深度学习 WEB网络 异常流量检测 混合神经网络 小波分解 

分 类 号：TP325[自动化与计算机技术—计算机系统结构]