Traffic classificationDeep learningTransfer learningScarce datasetInternet of Things (IoT) can provide the interconnection and data sharing among devices, vehicles, buildings via various sensors with the development of 5G, and it has been widely used in different services such as e-commerce, heath-...
Employing CNN, SAE, GRU, and LSTM as multi-task learning classification models, training validation and experimental testing were conducted on the QUIC dataset. A comparative analysis with single-task and ensemble learning methods reveals that, in the context of predicting network traffic types, the...
We tested our method on a well-known network traffic dataset and the results showed that our proposed method achieved better performance compared to a recent proposed method for handling imbalanced problem in network traffic classification. 展开 关键词: Auxiliary classifier GAN Deep learning Network ...
An improved BP neural network to identify traffic is proposed and MOORE_SET is used as dataset, meanwhile, building NOC_SET dataset based on CERNET(China Education and Research Network). the experiment results show that the accuracy rate of traffic classification based on the improved BP neural ...
The model named Deep Neural Networks was trained and tested with the dataset chosen to decide the optimized framework of the neural network for the effective classification of traffic on the network. Centralized learning, splitting learning, and federated learning models were proposed for the autonomous...
Identifying network traffic is essential, because plenty of information regarding a network flow can be learned by knowing the application protocol associated with it. However, the challenge for traffic classification is to identify features in the network flow data. This paper explores the issue of ...
Countering Machine-Learning Classification of Applications by Equalizing Network Traffic Statistics 概要: 我们提出了通过均衡主要的统计指标来降低基于有监督机器学习的网络流量分类器的对抗分类准确率。我们使用包的数量,间隔到达的时间,包长作为主要的流量指标,并估计他们用于应用识别时在分类准确率上的效果。网络流量分...
Adaptations of the input data with a suitable classification algorithm can give very good results in detecting and classifying anomalies in network traffic. The highest Conclusion In this paper, some of the classification algorithms were tested on the public UNSW-NB15 dataset used to test the IDS ...
Encrypted Traffic ClassificationWe tackle the problem of Encrypted Traffic Classification. We utilize the work of DeepPacket and use the ISCX Vpn 2016 Dataset to evaluate our work.Ref: https://arxiv.org/abs/1709.02656Ref: https://www.unb.ca/cic/datasets/vpn.html...
group the expected and actual results of all categories into the same table based on category. in this table, we can clearly observe the number of accurate and inaccurate recognitions for each category. dataset category classification the iscx dataset contains traffic characteristics and raw traffic ...