A Deep Learning Approach For Network Anomaly Detection based on AMF-LSTM
文献类型：会议论文
| 作者 | Mingyi Zhu; Kejiang Ye; Yang Wang; Cheng-Zhong Xu |
| 出版日期 | 2018 |
| 会议日期 | 2018 |
| 会议地点 | 日本 |
| 英文摘要 | The Internet and computer networks are currently suffering from different security threats. This paper presents a new method called AMF-LSTM for abnormal traffic detection by using deep learning model. We use the statistical features of multi-flows rather than a single flow or the features extracted from log as the input to obtain temporal correlation between flows, and add an attention mechanism to the original LSTM to help the model learn which traffic flow has more contributions to the final results. Experiments show AMF-LSTM method has high accuracy and recall in anomaly type identification. |
| 语种 | 英语 |
| URL标识 | 查看原文 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/14119]  |
| 专题 | 深圳先进技术研究院_数字所 |
| 推荐引用方式 GB/T 7714 | Mingyi Zhu,Kejiang Ye,Yang Wang,et al. A Deep Learning Approach For Network Anomaly Detection based on AMF-LSTM[C]. 见:. 日本. 2018. |
入库方式： OAI收割
来源：深圳先进技术研究院
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