本项目旨在设计和实现一个基于深度学习的加密恶意流量检测系统。通过将网络流量数据转换为图像数据，并利用图像分类模型进行检测，该系统能够有效地检测加密恶意流量。在数据集选取上使用了包含加密恶意和正常流量的数据集，以更好地反映实际网络环境中的特征和行为模式。在数据预处理方面，通过比较实验结果，选择了最佳的数据预处理方式进行流量特征提取。在模型选择方面，通过实验对比了几种常用的图像分类模型，并最终选择了GoogLeNet模型进行改进。通过引入注意力机制和深度可分离卷积等方法，提出了一种深度注意力Inception GoogLeNet（DA-Inception GoogLeNet）模型。实验结果表明，该模型在召回率和准确率上均有显著提高，对于特定类型的加密恶意流量的检测准确率提高了4.4%。
☆14Feb 28, 2024Updated 2 years ago
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