HAST-IDS: Learning Hierarchical Spatial-Temporal Features Using Deep Neural Networks to Improve Intrusion Detection
University of Science and Technology of China · Chinese Academy of Sciences · +2 more institutions
Abstract
The development of an anomaly-based intrusion detection system (IDS) is a primary research direction in the field of intrusion detection. An IDS learns normal and anomalous behavior by analyzing network traffic and can detect unknown and new attacks. However, the performance of an IDS is highly dependent on feature design, and designing a feature set that can accurately characterize network traffic is still an ongoing research issue. Anomaly-based IDSs also have the problem of a high false alarm rate (FAR), which seriously restricts their practical applications. In this paper, we propose a novel IDS called the hierarchical spatial-temporal features-based intrusion detection system (HAST-IDS), which first…
Citation impact
- FWCI
- 34.03
- Percentile
- 100%
- References
- 49
Authors
7- WWWei WangCorresponding
University of Science and Technology of China
- YSYiqiang Sheng
Chinese Academy of Sciences, Institute of Acoustics
- JWJinlin Wang
Chinese Academy of Sciences, Institute of Acoustics
- XZXuewen Zeng
Chinese Academy of Sciences, Institute of Acoustics
- XYXiaozhou Ye
Chinese Academy of Sciences, Institute of Acoustics
Topics & keywords
- Computer science
- Intrusion detection system
- Artificial intelligence
- Convolutional neural network
- Anomaly detection
- Deep learning
- Feature (linguistics)
- Artificial neural network