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。
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.
出处
《计算机工程》
CAS
CSCD
2013年第5期152-155,共4页
Computer Engineering
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
intrusion detection algorithm
Principal Component Analysis（PCA）
K-nearest neighbor algorithm
feature value
feature extraction
classifier
