• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International Conference on computer science education >Realization of intrusion detection system based on the improved data mining technology
【24h】

Realization of intrusion detection system based on the improved data mining technology

机译：基于改进数据挖掘技术的入侵检测系统实现

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

On the basis of further analyzing the operational mechanism of the existing intrusion detection system model, in allusion to the existing problem the powerless, high false negative rate, low detection efficiency and the lack of the rule base automatic extension mechanism to unknown aggressive behavior for existing detection mechanisms, Combining the relevant knowledge of data mining technology, then to design one improved network intrusion detection system model based on data mining, combined misuse detection and anomaly detection. In the model, we select the K-means algorithm in clustering analysis and the Apriori algorithm in association rule mining and improve it. Applying the improved K-means algorithm to achieve normal behavior classes and data separation module, then utilizing the improved Apriori algorithm to achieve automatic extension of the rule base. Finally, by the experiment to verify the function of the two algorithms.
机译：在进一步分析现有入侵检测系统模型的运行机制的基础上，阐明了现有的问题，无能为力，高假负率，低检测效率和缺乏规则基础自动延长机制，对现有的未知攻击行为检测机制，结合数据挖掘技术的相关知识，然后根据数据挖掘设计一个改进的网络入侵检测系统模型，滥用滥用检测和异常检测。在模型中，我们在关联规则挖掘中选择聚类分析和APRiori算法的K-means算法，改进。应用改进的k均值算法实现正常行为类和数据分离模块，然后利用改进的APRiori算法实现规则库的自动扩展。最后，通过实验来验证两个算法的功能。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
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