Ncrypted Traffic Classification News Today : Breaking News, Live Updates & Top Stories | Vimarsana

Network traffic classification (NTC) plays an important role in cyber security and network performance, for example in intrusion detection and facilitating a higher quality of service. However, due to the unbalanced nature of traffic datasets, NTC can be extremely challenging and poor management can degrade classification performance. While existing NTC methods seek to re-balance data distribution through resampling strategies, such approaches are known to suffer from information loss, overfitting, and increased model complexity. To address these challenges, we propose a new cost-sensitive deep learning approach to increase the robustness of deep learning classifiers against the imbalanced class problem in NTC. First, the dataset is divided into different partitions, and a cost matrix is created for each partition by considering the data distribution. Then, the costs are applied to the cost function layer to penalize classification errors. In our approach, costs are diverse in each typ ....

Class Imbalance , Convolutional Neural Networks , Cost Sensitive Learning , Deep Learning , Ncrypted Traffic Classification , Generative Adversarial Networks , Intrusion Detection , Task Analysis ,