基于数据挖掘和特征选择的入侵检测模型
A Network Intrusion Detection Model Based on Data Ming and Feature Selection Schemes
-
摘要: 提出了一种基于SVM特征选择和C4.5数据挖掘算法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,并提高检测速度.在经典的KDD 1999入侵检测数据集上的测试说明:该数据挖掘模型能够高效地对攻击模式进行训练学习,能够采用选择的特征正确有效地检测网络攻击.Abstract: This paper proposes a kind of intrusion detection model based on C4.5 data mining algorithm and SVM(correlation-based feature selection) based feature selection mechanism,which can effectively detect several types of attacks using the process of feature selection and attack feature training.The experiments on classic KDD 1999 intrusion dataset demonstrate our model is accurate and effective.
下载:
