A Novel Genetic Algorithm-XGBoost Based Intrusion Detection Method
文献类型：会议论文
| 作者 | Sun YY(孙莹莹)1,3; Song CH(宋纯贺)3,4,5,6 ; Yu SM(于诗矛)3,4,5,6; Pan H(潘昊)1; Li T(李桐)2; Liu Y(刘阳)3,4,5,6
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| 出版日期 | 2021 |
| 会议日期 | June 18-20, 2021 |
| 会议地点 | Chongqing, China |
| 关键词 | XGBoost genetic algorithm classification NSL-KDD prediction |
| 页码 | 51-57 |
| 英文摘要 | In order to improve the speed and accuracy of model intrusion detection in complex network environment, a network intrusion detection method based on genetic algorithm-optimized XGBoost is proposed. Taking the NSL-KDD data set as the object, the XGBoost model is trained with the ten-fold cross validation method, and the genetic algorithm is used to optimize the model parameters to predict and classify whether the network is attacked. It not only avoids the problem of low classification accuracy of basic machine learning models, but also solves the problem of time consuming and low efficiency in conventional grid search. The experimental results show that compared with other machine learning classification models, the proposed model can not only improve the accuracy of detection, but also save the time cost and achieve a more ideal classification effect. |
| 产权排序 | 1 |
| 会议录 | IEEE 4th Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2021
 |
| 会议录出版者 | IEEE |
| 会议录出版地 | New York |
| ISSN号 | 2693-2776 |
| ISBN号 | 978-1-7281-8534-7 |
| 源URL | [http://ir.sia.cn/handle/173321/29700]  |
| 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 | Song CH(宋纯贺) |
| 作者单位 | 1.Shenyang University of Chemical Technology, Shenyang 110142, China 2.Electric Power Research Institute of State Grid Liaoning Electric Power Co., Ltd 3.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China 4.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China 5.Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China 6.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China |
| 推荐引用方式 GB/T 7714 | Sun YY,Song CH,Yu SM,et al. A Novel Genetic Algorithm-XGBoost Based Intrusion Detection Method[C]. 见:. Chongqing, China. June 18-20, 2021. |
入库方式： OAI收割
来源：沈阳自动化研究所
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