| dc.contributor | 資訊系 | |
| dc.creator (作者) | 孫士勝 | |
| dc.creator (作者) | Sun, Shi-Sheng;Wang, Shang-Te | |
| dc.date (日期) | 2024-04 | |
| dc.date.accessioned | 7-Jan-2025 09:35:48 (UTC+8) | - |
| dc.date.available | 7-Jan-2025 09:35:48 (UTC+8) | - |
| dc.date.issued (上傳時間) | 7-Jan-2025 09:35:48 (UTC+8) | - |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/155065 | - |
| dc.description.abstract (摘要) | With the ever-increasing complexity of Internet security issues, new attack types can be detected every day. The Network Intrusion Detection System (NIDS) can quickly identify and deal with attacks. In this paper, we establish an intrusion detection model suitable for NIDS through incremental learning which can continuously learn the behaviors from new network flows. We utilize the UNSW_NB15 network intrusion dataset, and a portion of the data is used to train the initial model, while the remaining data simulates new network flows. Our proposed model can continuously learn new network flow patterns and adjust parameters without storing original data, while improving the model's training time and maintaining accuracy. | |
| dc.format.extent | 112 bytes | - |
| dc.format.mimetype | text/html | - |
| dc.relation (關聯) | 2024 IEEE 10th International Conference on Applied System Innovation, IEEE | |
| dc.subject (關鍵詞) | Incremental Learning; Network Intrusion Detection System; NIDS; UNSW-NB15 | |
| dc.title (題名) | An Incremental Learning Model for Network Intrusion Detection Systems | |
| dc.type (資料類型) | conference | |
| dc.identifier.doi (DOI) | 10.1109/ICASI60819.2024.10547921 | |
| dc.doi.uri (DOI) | https://doi.org/10.1109/ICASI60819.2024.10547921 | |
