...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>International journal on Semantic Web and information systems >Intrusion Detection Using Normalized Mutual Information Feature Selection and Parallel Quantum Genetic Algorithm
【24h】

Intrusion Detection Using Normalized Mutual Information Feature Selection and Parallel Quantum Genetic Algorithm

机译：Intrusion Detection Using Normalized Mutual Information Feature Selection and Parallel Quantum Genetic Algorithm

获取原文
获取原文并翻译 | 示例
   

获取外文期刊封面封底 >>

       
页面导航
  • 摘要
  • 著录项
  • 相关主题

摘要

This paper presents a detection algorithm using normalized mutual information feature selection and cooperative evolution of multiple operators based on adaptive parallel quantum genetic algorithm (NMIFS MOP-AQGA). The proposed algorithm is to address the problems that the intrusion detection system (IDS) has lower detection speed, less adaptability, and lower detection accuracy. In order to achieve an effective reduction for high-dimensional feature data, the NMIFS method is used to select the best feature combination. The best features are sent to the MOP-AQGA classifier for learning and training, and the intrusion detectors are obtained. The data are fed into the detection algorithm to ultimately generate accurate detection results. The experimental results on real abnormal data demonstrate that the NMIFS MOP-AQGA method has higher detection accuracy, lower false negative rate, and higher adaptive performance than the existing detection methods, especially for small samples sets.

著录项

获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号