我们的特征集包括下面评价指标的最小值、最大值、平均值、中位数、方差:数据包间隔时间、数据包长、TCP窗口大小,以及下面的统计特征:包数量、总的流长、源和目的IP地址、源和目的端口号。 数据集公开可用:[61] S. Fathi-Kazerooni and R. Rojas-Cessa, PCAP Dataset of Applications Traffic Captured with Wire...
The device generates a training dataset based on the training dataset dictionary for training a machine learning-based traffic flow analyzer.ANDERSON Blake HarrellMcGREW David
Section 2 gives the related work on APP-ID as well as the use of DL models for traffic classification. In Section 3, we describe the methodology of our work including dataset collection approach and applied DL models. We give the evaluation results and discussion in Section 4 and model ...
The motivation and objective of the research are to correctly and as securely as possible classify network traffic anomalies in the labeled dataset using classification as one of the machine learning methods. This paper includes the preliminary research and overview of the classification methods, testing...
It exhibits a high degree of stealth and obfuscation in cyberspace, making it difficult for Network Intrusion Detection System(NIDS) to ensure the accuracy and timeliness of detection. This paper researches machine learning and deep learning for intrusion detection in imbalanced network traffic. It ...
5G cellular networks have recently fostered a wide range of emerging applications, but their popularity has led to traffic growth that far outpaces network expansion. This mismatch may decrease network quality and cause severe performance problems. To reduce the risk, operators need long term traffic...
To use the UNSW-NB15 data set to classify cyber attack. It is an IoT-based dataset that contains network traffic captured by a residential gateway. iotmachine-learningcybersecuritycyberattacknetworktrafficanalysis UpdatedMay 2, 2023 Jupyter Notebook ...
Reducing dependence on extensive labeled data: the proposed multi-task learning framework reduces the need for a large labeled traffic dataset. (5) Experimental validation: the paper conducts two experiments using the QUIC public dataset, demonstrating the efficacy of the proposed method. The structur...
We first train the model for non-evacuation period traffic data and found that the model outperforms existing deep learning models for predicting non-evacuation period traffic with an RMSE value of 226.84. However, when the model is applied for predicting evacuation traffic, the RMSE value ...
importtorch.nn.functionalasFdata=dataset[0]optimizer=torch.optim.Adam(model.parameters(),lr=0.01)forepochinrange(200):pred=model(data.x,data.edge_index)loss=F.cross_entropy(pred[data.train_mask],data.y[data.train_mask])# Backpropagationoptimizer.zero_grad()loss.backward()optimizer.step() ...