基于云计算入侵检测数据集的内网用户异常行为分类算法研究

Research on Anomaly Behavior Classification Algorithm of Internal Network User Based on Cloud Computing Intrusion Detection Data Set

针对在云计算内网环境下实施入侵检测与用户异常行为分析的难题,文章采用Weka机器学习软件工具自带的典型分类算法对云计算入侵检测数据集进行分类研究,并通过软件工程方法实现了用于内网用户异常行为分类的朴素贝叶斯算法。对恶意行为和正常行为分类的实验结果显示,文章所实现的朴素贝叶斯算法具有较高的分类准确度,可以有效地对云计算入侵检测数据集中的内网用户行为进行分类分析与挖掘,证明了文章所提方案和算法的有效性。

In view of the problems of the implementation of intrusion detection and analysis of abnormal behavior under the cloud computing internal network environment, this paper does the classification research on the cloud intrusion detection datasets（CIDD） by using Weka machine learning classification algorithms, and realizes naive Bayesian algorithm for abnormal behavior classification of internal network users through the method of software engineering. Experimental results on the classification of malicious behavior and normal behavior show that the naive Bayesian algorithm implemented in the paper achieves higher classification accuracy. The algorithm can effectively classify and analyze the internal network user behaviors of CIDD, which proves the effectiveness of the proposed scheme and algorithm.