期刊論文
| 學年 | 113 |
|---|---|
| 學期 | 2 |
| 出版（發表）日期 | 2025-03-14 |
| 作品名稱 | Enhancing network intrusion detection by employing Mondrian forests to achieve multiple attack classification |
| 作品名稱（其他語言） | |
| 著者 | Chuang, Po‑jen; Huang, Pang‑yu |
| 單位 | |
| 出版者 | |
| 著錄名稱、卷期、頁數 | The Journal of Supercomputing 81, 617 |
| 摘要 | In online intrusion detection, a classification model able to identify different types of attacks is valuable as it can help users respond instantly and adequately against unexpected adversaries. This paper presents a new active learning mechanism to secure effective multiple attack classification for online intrusion detection. The new mechanism is built over our previous lifelong sampling (LS) mechanism which uses its random forest (RF) operation to pursue favorable binary classification in online environments. The new mechanism advances the LS-RF framework by using the Mondrian forest (MF)—an innate lifelong learning procedure able to avoid cumulative training data without additional effort—to develop a desired multiple classification architecture. We choose MF to realize online multiple classification mainly because it can train a model to identify different attack types in the online process and can hence fortify classification and detection to help users act promptly against sudden maliciousness. Experimental results show that, by effective model training, our Multi-Classification mechanism performs desirable classification and detection—in terms of precision, accuracy and F1-scores—at moderate time cost (which is feasible and negligible when compared with the significant performance gain). |
| 關鍵字 | Network intrusion detection;Active learning;Lifelong learning;Online learning;Binary and multiple classification;Random forests;Mondrian forests;Performance evaluation |
| 語言 | en |
| ISSN | 1573-0484 |
| 期刊性質 | 國外 |
| 收錄於 | SCI |
| 產學合作 | |
| 通訊作者 | |
| 審稿制度 | 是 |
| 國別 | USA |
| 公開徵稿 | |
| 出版型式 | ,電子版,紙本 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/126935 ) |