基于深度学习的网络应用加密流量识别方法研究
时间: 2023-12-07 07:02:50 浏览: 60
摘要:
网络应用加密流量识别一直是网络安全领域中的重要问题之一。传统的加密流量识别方法主要基于特征提取和机器学习算法,但是这些方法存在着高维特征提取困难、易受攻击等问题。本文提出了一种基于深度学习的网络应用加密流量识别方法。该方法通过卷积神经网络对加密流量进行特征提取,结合全连接神经网络进行分类。实验结果表明,本方法能够有效地识别不同的网络应用加密流量,具有较高的准确率和鲁棒性。
关键词:深度学习;加密流量识别;卷积神经网络;全连接神经网络
Abstract:
Network application encrypted traffic identification has always been one of the important problems in the field of network security. Traditional encrypted traffic identification methods are mainly based on feature extraction and machine learning algorithms, but these methods have problems such as difficult high-dimensional feature extraction and vulnerability to attacks. This paper proposes a network application encrypted traffic identification method based on deep learning. This method uses convolutional neural networks to extract features from encrypted traffic and combines them with fully connected neural networks for classification. Experimental results show that this method can effectively identify different network application encrypted traffic, with high accuracy and robustness.
Keywords: deep learning; encrypted traffic identification; convolutional neural network; fully connected neural network