• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>Intelligence and Security Informatics; Lecture Notes in Computer Science; 4430 >TCM-KNN Algorithm for Supervised Network Intrusion Detection
【24h】

TCM-KNN Algorithm for Supervised Network Intrusion Detection

机译：TCM-KNN监督网络入侵检测算法

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Intrusion detection is a hot topic related to information and national security. Supervised network intrusion detection has been an active and difficult research hotspot in the field of intrusion detection for many years. However, a lot of issues haven't been resolved successfully yet. The most important one is the loss of detection performance attribute to the difficulties in obtaining adequate attack data for the supervised classifiers to model the attack patterns, and the data acquisition task is always time-consuming which greatly relies on the domain experts. In this paper, we propose a novel network intrusion detection method based on TCM-KNN (Transductive Confidence Machines for K-Nearest Neighbors) algorithm. Experimental results on the well-known KDD Cup 1999 dataset demonstrate the proposed method is robust and more effective than the state-of-the-art intrusion detection method even provided with "small" dataset for training.
机译：入侵检测是与信息和国家安全相关的热门话题。多年来，受监督的网络入侵检测一直是入侵检测领域中一个活跃而又困难的研究热点。但是，许多问题尚未成功解决。最重要的一个是检测性能的损失，这归因于难以获得足够的攻击数据以供监督分类器建模的攻击模式，并且数据获取任务总是很耗时，这在很大程度上取决于领域专家。在本文中，我们提出了一种基于TCM-KNN（K-最近邻直觉置信机）算法的新型网络入侵检测方法。在著名的KDD Cup 1999数据集上的实验结果表明，该方法比最新的入侵检测方法（即使提供了用于训练的“小”数据集）也更强大，更有效。

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