Details

Autor(en) / Beteiligte

• Li, Yanmiao
• Kong, Xuan
• Hou, Jiangang
• Li, Xin
• Zhao, Kun
• Liang, Wei
• Jiang, Tongqing
• Xin, Yang
• Liu, Zhi

Titel

NIN-DSC: A Network Traffic Anomaly Detection Method Based on Deep Learning

Ist Teil von

• 2022 7th International Conference on Signal and Image Processing (ICSIP), 2022, p.390-394

Ort / Verlag

IEEE

Erscheinungsjahr

2022

Quelle

IEEE Xplore

Beschreibungen/Notizen

• Network traffic anomaly detection plays an important role in maintaining network space security and is usually modeled as a traffic classification problem. With the rapid change of the Internet, the network traffic environment is increasingly complex, and the traditional classification method is no longer applicable. In this article, we propose a new traffic classification framework for network anomaly detection, which operates directly on the original traffic. The NIN-DSC network was used to extract the features of the data, and the validation was carried out in the public data set.