融合NBC与PNN的网络异常分类

对网络异常进行分类有利于管理员更好地管理网络，然而单一的分类器存在对各类异常的分类效果不均衡，不够全面等问题。鉴于此在研究了常用于分类的概率神经网络（Probability Neural Network，PNN）算法和朴素贝叶斯分类器（Naive Bayes Classifier，NBC）算法的基础上提出了一种融合NBC与PNN的网络异常分类模型。该模型将PNN与NBC对各类网络异常的分类精度作为权值，通过计算得出未知流量所属各类别的概率，最大值为预测结果，通过KDD99数据集对该模型进行测试，实验结果表明，提出的新模型相对于仅使用PNN或者NBC的单分类器，其对各类异常的分类效果具有更好的均衡性和更高的分类精度。

Anomaly classification based on fusion of NBC and PNN

Classifying the network anomalies will help the administrators manage the network better. However, the single classifier has the problem that the results of the classification of the various of anomalies are not balanced, not comprehensive and other issues. In consideration of these facts, based on the research of the PNN algorithm and the NBC algorithm which are the most frequently used in classification filed, it proposes a new model using the fusion of the two algorithms. This model uses the accuracy of PNN and NBC that to classify the anomalies as weights, by calculating to obtain the?probability that belongs to each category of?the unknown flow, and the biggest probability will be choosed as the result. According to the verification of the KDD99 data set, experimental results show that the proposed model has the better classification rate and better balance than the simple classifier which through the PNN or NBC algorithm.