Research on Intrusion Detection Based on BP Neural Network
文献类型：会议论文
| 作者 | Chen, Haonan1; Liu YY(刘意杨)2 ; Zhao JM(赵剑明)2; Liu XD(刘贤达)2
|
| 出版日期 | 2021 |
| 会议日期 | January 15-17, 2021 |
| 会议地点 | Virtual, Guangzhou, China |
| 关键词 | BP neural network intrusion detection back propagation |
| 页码 | 79-82 |
| 英文摘要 | The purpose of network security is to prevent the data transmitted over the Internet from being stolen and tampered with, and to ensure the security of the data. It is not only necessary to ensure that the information entering and exiting the network is not stolen or tampered with, but also to ensure the integrity and confidentiality of the information in the information system. The network environment is becoming more and more complex, and the attack methods are becoming more and more diverse. Therefore, intrusion detection systems have some common problems, such as low detection rate and high false alarm rate, and it is difficult to meet the real-time requirements of intrusion detection systems. Currently, deep learning is increasingly used in intrusion detection. In order to solve the problems existing in the current intrusion detection system, this paper studies the application of deep learning in intrusion detection. First, it analyzes the BP neural network (BP-NN) technology, and proposes an improvement method for the shortcomings of the current BP-NN, and finally conducts an empirical analysis. Experimental results show that intrusion detection based on BP-NN has a high accuracy rate, and the false alarm rate and false alarm rate are both at a low level. |
| 源文献作者 | IEEE |
| 产权排序 | 2 |
| 会议录 | 2021 IEEE International Conference on Consumer Electronics and Computer Engineering, ICCECE 2021
 |
| 会议录出版者 | IEEE |
| 会议录出版地 | New York |
| 语种 | 英语 |
| ISBN号 | 978-1-7281-8319-0 |
| WOS记录号 | WOS:000680655600014 |
| 源URL | [http://ir.sia.cn/handle/173321/28347]  |
| 专题 | 沈阳自动化研究所_工业控制网络与系统研究室 |
| 通讯作者 | Liu YY(刘意杨) |
| 作者单位 | 1.College of Information Science and Engineering, Northeastern University, Shenyang, China 2.Industrial Control Network and System Laboratory, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China |
| 推荐引用方式 GB/T 7714 | Chen, Haonan,Liu YY,Zhao JM,et al. Research on Intrusion Detection Based on BP Neural Network[C]. 见:. Virtual, Guangzhou, China. January 15-17, 2021. |
入库方式： OAI收割
来源：沈阳自动化研究所
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