Abstract: Abstract: 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.

Key words: Key words: convolutional neural network, cost-sensitive learning, encrypted traffic classification, class-imbalance, loss function