摘要
当前的网络入侵检测技术存在误报率、漏报率高,资源负载重,自适应性、智能化程度低,可扩展性差等问题,针对这些问题,本文提出了一种基于免疫原理的网络入侵检测模型,具有检测率高、资源负载轻、高自适应、高智能化、可扩展、可调节、高鲁棒性等优点,通过仿真实验验证了该模型中所采用算法和机制的有效性。
Some problems such as high false positive rate and false negative rate, heavyweight, low adaptability and automatism and poor scalability, exists in current network-based intrusion detection technologies. To solve them, this paper presents an immo-inspired network-based intrusion detection model which is with high detection rate, lightweight, adaptive, self-learning, scalable, adjustable, robust. Algorithms and mechanisms used in the model are justified as effective by experimental results.
出处
《微计算机信息》
2009年第9期53-55,共3页
Control & Automation
关键词
入侵检测
免疫
否定选择
克隆选择
Intrusion Detection
Immunity
Negative Selection
Clonal Selection
