• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International Conference on Computational Scinece and Its Applications(ICCSA 2005) pt.1; 20050509-12; Singapore(SG) >A Hybrid Network Model for Intrusion Detection Based on Session Patterns and Rate of False Errors
【24h】

A Hybrid Network Model for Intrusion Detection Based on Session Patterns and Rate of False Errors

机译：基于会话模式和错误率的混合入侵检测网络模型

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

Nowadays, computer network systems play an increasingly important role in our society. They have become the target of a wide array of malicious attacks that can turn into actual intrusions. This is the reason why computer security has become an essential concern for network administrators. Intrusions can wreak havoc on LANs. And the time and cost to repair the damage can grow to extreme proportions. Instead of using passive measures to fix and patch security holes, it is more effective to adopt proactive measures against intrusions. Recently, several IDS have been proposed and they are based on various technologies. However, these techniques, which have been used in many systems, are useful only for detecting the existing patterns of intrusion. It can not detect new patterns of intrusion. Therefore, it is necessary to develop new technology of IDS that can find new pattern of intrusion. In this paper, we propose a hybrid network model for IDS based on reducing risk of false negative errors and false positive errors that can detect intrusion in the forms of the denial of service and probe attack detection method by measuring the resource capacities. The "IDS Evaluation Data Set" made by MIT was used for the performance evaluation.
机译：如今，计算机网络系统在我们的社会中扮演着越来越重要的角色。它们已成为各种恶意攻击的目标，这些恶意攻击可能转化为实际的入侵。这就是为什么计算机安全已成为网络管理员必备的原因。入侵会给LAN造成严重破坏。而且修复损坏的时间和成本可能会增长到极高的比例。与其采取被动措施来修复和修补安全漏洞，不如采取主动措施来防止入侵。最近，已经提出了几种基于各种技术的IDS。但是，已在许多系统中使用的这些技术仅用于检测现有的入侵模式。它无法检测到新的入侵模式。因此，有必要开发能够发现新的入侵模式的IDS新技术。在本文中，我们提出了一种基于IDS的混合网络模型，该模型基于减少误报错误和误报错误的风险，可以通过拒绝服务的形式检测入侵并通过测量资源容量来探测攻击检测方法。 MIT使用的“ IDS评估数据集”用于性能评估。

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