基于特征加权朴素贝叶斯算法的网络用户识别

基于网络用户的访问记录，提出了采用特征加权的朴素贝叶斯分类算法对用户进行识别。首先利用基于WinPcap框架的数据采集系统对用户访问记录进行采集，通过分析记录从5个方面对用户特征进行统计，并经过筛选后对特征进行选取，最后采用特征加权的朴素贝叶斯分类算法对3300个测试样本进行识别，识别率达到了85.73%。实验结果表明该算法能够有效实现对网络用户身份的识别。

Network user identification based feature weighting naive Bayesian classification algorithm

Based on the access logs of network users, Feature Weighting Naive Bayes Classification(FWNBC) algorithm is used to identify users. Firstly, the data acquisition system based on WinPcap framework was used to collect the access logs of network users, characteristics are counted from five aspects by analyzing these access logs, and then selected after filtering, at last the FWNBC algorithm is used to identify the 3300 samples, and the recognition rate reached 85.73%.The experiment results show that this algorithm is effective to identify the identity of network users.