一种用于入侵检测系统的并联GBDT二次集成学习
文献类型：期刊论文
| 作者 | 杨骏; 盛益强; 王劲林 |
| 刊名 | IEEE Access
 |
| 出版日期 | 2020 |
| 期号 | 未知页码:175467 |
| ISSN号 | 2169-3536 |
| DOI | 10.1109/ACCESS.2020.3026044 |
| 英文摘要 | The development of computer and network technology has provided convenience to our daily life, however, attack and intrusion in network emerge endlessly. Intrusion Detection System (IDS) has been developed to confront network attacks. As a result, the research of IDS is one of the most popular fields in recent years. This paper proposes a Gradient Boosting Decision Tree (GBDT)-paralleled quadratic ensemble learning method for intrusion detection system. We use GBDT to deal with the spatial part of traffic data and use Gated Recurrent Unit (GRU) model with special modification for network traffic to deal with temporal data. Then, in order to combine the spatial feature and temporal feature, we fuse GBDT model and GRU model to make a quadratic ensemble model as our final intrusion detection system. The experimental results based on CICIDS2017 dataset show that the advanced spatial-temporal intrusion detection system based on ensemble learning achieves better accuracy, recall, precision and F1 score than the state-of-the-art methods. The accuracies of detecting benign, port scan, Distributed Denial of Service (DDoS), infiltration and web attack traffic are up to 99.9%, 99.9%, 99.9%, 99.9%, and 99.9%, respectively. We also use our method in Information-Centric Networking (ICN) dataset and the results show our method achieves much better performance compared with existing methods. |
| URL标识 | 查看原文 |
| 源URL | [http://159.226.59.140/handle/311008/9531]  |
| 专题 | 历年期刊论文_2020年期刊论文 |
| 作者单位 | 中国科学院声学研究所 |
| 推荐引用方式 GB/T 7714 | 杨骏；盛益强；王劲林. 一种用于入侵检测系统的并联GBDT二次集成学习[J]. IEEE Access,2020(未知):175467. |
| APA | 杨骏；盛益强；王劲林.(2020).一种用于入侵检测系统的并联GBDT二次集成学习.IEEE Access(未知),175467. |
| MLA | 杨骏；盛益强；王劲林."一种用于入侵检测系统的并联GBDT二次集成学习".IEEE Access .未知(2020):175467. |
入库方式： OAI收割
来源：声学研究所
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