摘要
半监督的双协同训练要求划分出的2个数据向量相互独立,不符合真实的网络入侵检测数据特征。为此,提出一种基于三协同训练(Tri-training)的入侵检测算法。使用大量未标记数据,通过3个分类器对检测结果进行循环迭代训练,避免交叉验证。仿真实验表明,在少量样本情况下,该算法的检测准确度比SVM Co-training算法提高了2.1%,并且随着循环次数的增加,其性能优势更加明显。
The Co-training method requires the independence of two data vectors, which is far from the characteristic of real dataset in network intrusion detection. This paper proposes a intrusion detection method based on Tri-training. It exploits the large amount of unlabeled data, and increases the detection accuracy and stability by Co-training three classifiers. Simulation results show that this method is 2.1% more accurate than the SVM Co-training method, and it performs better with the increase of the loop number.
出处
《计算机工程》
CAS
CSCD
2012年第6期158-160,共3页
Computer Engineering
基金
国家自然科学基金资助项目(61103015)
湖南省自然科学基金资助项目(09JJ5043)
关键词
入侵检测
小样本
支持向量机
半监督
双协同训练
三协同训练
intrusion detection
small-sample
Support Vector Machine（SVM）
semi-supervised
Co-training
Tri-training
