微型电脑应用2023，Vol.39Issue(12)：151-154,158,5.
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
徐丞1
作者信息
- 1. 成都鸿安华宇科技有限公司,四川,成都 610000
- 折叠
摘要
Abstract
In order to keenly perceive the network environment situation and improve the security of user privacy data,a deep learning-based network web abnormal traffic detection method is proposed.We use the data plane development toolkit to design the Web traffic collection architecture,set up core components such as the environment abstraction layer,memory management layer,and debugging components to capture traffic packets in real time.The data signal is subjected to discrete wavelet trans-form,the sliding window is set,the numerical sequence is decomposed,and the characteristic parameters such as average val-ue,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 detec-tion,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Key words
deep learning/Web network/abnormal traffic detection/hybrid neural network/wavelet decomposition分类
计算机与自动化引用本文复制引用
徐丞..基于深度学习的网络Web异常流量检测方法[J].微型电脑应用,2023,39(12):151-154,158,5.
