HAST-IDS: Learning Hierarchical Spatial-Temporal Features Using Deep Neural Networks to Improve Intrusion Detection

@article{Wang2018HASTIDSLH,
  title={HAST-IDS: Learning Hierarchical Spatial-Temporal Features Using Deep Neural Networks to Improve Intrusion Detection},
  author={Wei Wang and Yiqiang Sheng and Jinlin Wang and Xuewen Zeng and Xiaozhou Ye and Yongzhong Huang and Ming Zhu},
  journal={IEEE Access},
  year={2018},
  volume={6},
  pages={1792-1806}
}
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… CONTINUE READING
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