| Title |
Deepwindow: An Efficient Method For Online Network Traffic Anomaly Detection |
| ID_Doc |
18152 |
| Authors |
Shi Z.; Li J.; Wu C.; Li J. |
| Year |
2019 |
| Published |
Proceedings - 21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2019 |
| DOI |
http://dx.doi.org/10.1109/HPCC/SmartCity/DSS.2019.00335 |
| Abstract |
With the explosion of network traffic volume, high efficient and large-scale network traffic anomaly detection methods becomes necessary. However, existing methods often fail to take into account both the detection delay and the detection accuracy. We propose a novel method, focusing on period-wise detection. We use Long Short-Term Memory (LSTM) to establish abnormal traffic detection model. Besides, We use some big data processing frameworks for online network traffic collection and preprocessing. Performance evaluation shows that our online anomaly detection model outperforms other anomaly detection methods based on traditional anomaly detection methodologies. © 2019 IEEE. |
| Author Keywords |
anomaly detection; deep learning; intrusion detection system; network traffic; neural network |
