Novel intrusion detection system integrating layered framework with neural network

N. Srivastav, R. Challa
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引用次数: 39

Abstract

The threat from spammers, attackers and criminal enterprises has grown with the expansion of Internet, thus, intrusion detection systems (IDS)have become a core component of computer network due to prevalence of such threats. In this paper, we present layered framework integrated with neural network to build an effective intrusion detection system. This system has experimented with Knowledge Discovery & Data Mining(KDD) 1999 dataset. The systems are compared with existing approaches of intrusion detection which either uses neural network or based on layered framework. The results show that the proposed system has high attack detection accuracy and less false alarm rate.
将分层框架与神经网络相结合的入侵检测系统
随着互联网的发展,来自垃圾邮件发送者、攻击者和犯罪企业的威胁越来越大,入侵检测系统(IDS)也因此成为计算机网络的核心组成部分。本文提出了结合神经网络的分层框架来构建有效的入侵检测系统。该系统以知识发现与数据挖掘(KDD) 1999数据集为实验对象。将该系统与现有的基于神经网络和分层框架的入侵检测方法进行了比较。结果表明,该系统具有较高的攻击检测准确率和较低的虚警率。
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