摘要
针对传统的基于遗传神经网络的入侵检测模型未考虑误分类代价的不足,将误分类代价敏感的特征集成到基于遗传神经网络的网络入侵检测模型中,从而克服了传统模型中错误分类时可能导致代价过大的缺点。通过实验结果表明,增加了误分类代价敏感特征后的遗传神经网络能较好地控制网络入侵检测系统误报、漏报攻击时所产生的代价。
The paper aims at the insufficient of traditional intrusion detection based on genetic neural network not consider the misclassification cost, integrate the misclassification cost-sensitive features into the network intrusion detection model which based on genetic neural network, to overcome the defect of the traditional model＇s error classifying result in excessive costs. The experiment results show that after the genetic neural network increased the misclassification cost-sensitive features, it can control the cost caused by the network intrusion detection＇s false report, omit report attacks preferably.
出处
《计算机系统应用》
2011年第6期49-51,48,共4页
Computer Systems & Applications
关键词
入侵检测
遗传算法
神经网络
误分类代价
intrusion detection
genetic algorithm
neural network
misclassification cost
