摘要: 为提高入侵检测的效率和准确率,提出一种基于主成分分析法和K-最近邻算法的入侵检测算法。对原始攻击数据按其攻击类型的不同,分别利用主成分分析提取特征值,并通过K-最近邻算法对测试数据进行分类。Matlab仿真结果表明,将训练数据进行分类后再进行特征提取,能有效降低数据维数,提高分类算法的准确率。
关键词:
入侵检测算法,
主成分分析,
K-最近邻算法,
特征值,
特征提取,
分类器
Abstract: To improve the efficiency and veracity of the intrusion detection, this paper presents an intrusion detection algorithm based on Principal Component Analysis(PCA) and K-nearest neighbor algorithm. This algorithm classifies the original attack data ordering by the class of attack, and extracts each class features based on the PCA. It uses the K-nearest neighbor algorithm to classify the observational data. Matlab simulations experiments result shows that this algorithm can effectively decrease the data dimension and enhance the veracity.
Key words:
intrusion detection algorithm,
Principal Component Analysis(PCA),
K-nearest neighbor algorithm,
feature value,
feature extraction,
classifier
中图分类号:
李占波, 白全海, 申义彩. 基于主成分分析的网络入侵检测算法[J]. 计算机工程, 2013, 39(5): 152-155.
LI Tie-Bei, BAI Quan-Hai, SHEN Xi-Cai-. Network Intrusion Detection Algorithm Based on Principal Component Analysis[J]. Computer Engineering, 2013, 39(5): 152-155.