基于代价敏感卷积神经网络的加密流量分类

Cost-sensitive Convolutional Neural Network for Encrypted Traffic Classification

针对加密流量分类中由于不平衡数据导致的分类偏差和少数类识别率低的问题,提出一种基于代价敏感卷积神经网络的加密流量分类方法。鉴于传统卷积神经网络在处理不平衡数据时容易偏向多数类,该方法引入动态权重调整策略,使其在每次迭代中根据代价敏感层的反馈来重新评估并自适应调整每个样本的权重。当少数类样本被模型误分类时,其权重会增加,促使模型在后续训练中更加关注它们。随着训练的进行,这种动态权重调整策略持续驱使模型改进并提高对少数类样本的识别能力,从而有效地应对类别不平衡问题。为了避免过拟合,该方法还采纳早停策略,当验证集性能连续下滑时及时终止训练。实验结果表明,本文所提出的网络模型在处理类别不平衡的加密流量分类问题上具有显著的优势,准确率和F1值均达到0.97以上。本文研究为加密流量分类提供了一种更为有效且适应于类别不平衡问题的解决方案,为网络安全领域的研究与应用提供了有益的探索。

This paper addresses classification bias and low recognition rates for minority classes in encrypted traffic classification arising from imbalanced data.Traditional convolutional neural networks tend to favor the majority class in such scenarios,prompting a dynamic weight adjustment strategy.In this approach,during each training iteration,sample weights are adaptively adjusted based on feedback from the cost-sensitive layer.If a minority class sample is misclassified,its weight increases,urging the model to focus on such samples in future training.This strategy continually refines the model’s predictions,enhancing minority class recognition and effectively tackling class imbalance.To prevent overfitting,an early stopping strategy is employed,halting training when validation performance deteriorates consecutively.Experiments reveal that the proposed model significantly excels in addressing class imbalance in encrypted traffic classification,achieving accuracy and F1 scores over 0.97.This study presents a potential solution for encrypted traffic classification amidst class imbalance,contributing valuable insights to network security.