基于神经网络和CFS特征选择的网络入侵检测系统

A Network Intrusion Detection System Based on Neural Networks and the CFS-Based Feature Selection

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 （correlationbased 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.