.png){kind=icon}
返回
Network Traffic Classification Using Deep Learning Networks and Bayesian Data Fusion
.png){kind=icon}
PRE
AI
DOI：10.1007/s10922-021-09639-z.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The rapid growth of current computer networks and their applications has made network traffic classification more important. The latest approach in this field is the use of deep learning. But the problem of deep learning is that it needs a lot of data for training. On the other hand, the lack of a sufficient amount of data for different types of network traffic has a negative effect on the accuracy of the traffic classification. In this regard, one of the appropriate solutions to address this challenge is the use of data fusion methods in decision level. Data fusion techniques make possible to achieve better results by combining classifiers. In this paper, a network traffic classification approach based on deep learning and data fusion techniques is presented. The proposed method can identify encrypted traffic and distinguish between VPN and non-VPN network traffic. In the proposed approach, first, a preprocessing on the dataset is carried out, then three deep learning networks, namely, Deep Belief Network, Convolution Neural Network, and Multi-layer Perceptron to classify network traffic are employed. Finally, the results of all three classifiers using Bayesian decision fusion are combined. The experimental results on the ISCX VPN-nonVPN dataset show that the proposed method improves the classification accuracy and performs well on different network traffic types. The average accuracy of the proposed method is 97%.
Keyword:
Encrypted traffic identification
Decision level
Data fusion
Deep learning
Traffic classification
期刊
IF:
3.9
论文数:
1.0K
被引数:
1.3K
机构
引用论文
Presenting Concerns of Veterans Entering Treatment for Posttraumatic Stress Disorder提出退伍军人进入创伤后应激障碍治疗的担忧
PREAI
分享收藏Deep-Full-Range: A Deep Learning Based Network Encrypted Traffic Classification and Intrusion Detection FrameworkDeep-Full-Range: 基于深度学习的网络加密流量分类和入侵检测框架.png)
IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAI分享收藏Automatic Mobile App Identification From Encrypted Traffic With Hybrid Neural NetworksIEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏
查看更多内容