基于直推式方法的网络异常检测方法  

A Network Anomaly Detection Method Based on Transduction Scheme

作　　者：李洋[1] 方滨兴[1] 郭莉[1] 陈友[1] 

机构地区：[1]中国科学院计算技术研究所

出　　处：《软件学报》2007年第10期2595-2604,共10页Journal of Software

基　　金：Supported by the National Natural Science Foundation of China under Grant No.60573134 (国家自然科学基金);the National Information Security 242 Project of China under Grant No.2005C39 (国家 242 信息安全计划项目)

摘　　要：网络异常检测技术是入侵检测领域研究的热点和难点内容,目前仍然存在着误报率较高、对建立检测模型的数据要求过高、在复杂的网络环境中由于"噪音"的影响而导致检测率不高等问题.基于改进的TCM-KNN(transductive confidence machines for K-nearest neighbors)置信度机器学习算法,提出了一种网络异常检测的新方法,能够在高置信度的情况下,使用训练的正常样本有效地对异常进行检测.通过大量基于著名的KDD Cup1999数据集的实验,表明其相对于传统的异常检测方法在保证较高检测率的前提下,有效地降低了误报率.另外,在训练集有少量"噪音"数据干扰的情况下,其仍能保证较高的检测性能;并且在采用"小样本"训练集以及为了避免"维灾难"而进行特征选取等优化处理后,其性能没有明显的削减.Network anomaly detection has been an active and difficult research topic in the field of intrusion detection for many years. Up to now, high false alarm rate, requirement of high quality data for modeling the normal patterns and the deterioration of detection rate because of some ＂noisy＂ data in the training set still make it not perform as well as expected in practice. This paper presents a novel network anomaly detection method based on improved TCM-KNN （transductive confidence machines for K-nearest neighbors） machine learning algorithm, which can effectively detect anomalies using normal data for training. A series of experiments on well known KDD Cup 1999 dataset demonstrate that it has lower false positive rate, especially higher confidence under the condition of ensuring high detection rate than the traditional anomaly detection methods. In addition, even provided with training dataset contaminated by ＂noisy＂ data, the proposed method still holds good detection performance. Furthermore, it can be optimized without obvious loss of detection performance by adopting small dataset for training and employing feature selection aiming at avoiding the ＂curse of dimensionality＂.

关 键 词：网络安全 异常检测 奇异值 直推式信度机 TCM—KNN算法 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]