本文提出了一种新型的基于CFS特征选择和神经网络的高效入侵检测模型。通过使用该模型对经过特征提取后的攻击数据的训练学习，可以有效地识别各种入侵。在经典的KDD Cup 1999入侵检测数据集上的测试说明，该模型能够高效地对攻击模式进行训练学习，从而正确有效地检测网络攻击。

This paper introduces a novel intrusion detection model based on neural networks and the CFS (correlationbased feature selection) based feature selection mechanism. It can effectively detect several types of attacks by combining neural networks and the CFSbased feature selection. The experiments upon the wellknown KDD Cup 1999 intrusion detection dataset demonstrate that the model is actually effective in practice.