【摘要】 针对目前入侵检测系统误报率过高、检测率不高和对未知入侵检测能力有限的缺陷,提出一种基于模糊SOFM的网络入侵检测方法,经训练后可形成一个稳定的神经网络系统,有效地识别网络正常行为和异常行为。采用KDD99数据集对系统进行实验,结果表明,系统在保持误报率低于3%的情况下,入侵检测率最高可以达到92%以上。更多还原

【Abstract】 Considering current intrusion detection system with high misinformation rate and low detection rate,this paper applies fuzzy Self-Organizing Feature Map(SOFM) neural network to intrusion detection. After being trained,the fuzzy SOFM network can become a stable nerve network system and identify a network normal and abnormal behavior effectively. Experimental results show that using the KDD99 databases,intrusion detection rate is more than 92% when the misinformation rate is below 3%.更多还原