基于计算似然比的分布外网络流量数据检测方法

Out-of-Distribution Network Traffic Data Detection Technology Based on Calculation of Likelihood Ratio

深度学习模型有时会将一些未知类别数据误分类为已知类别,这些未知类别数据定义为在某些领域的分布外数据,例如生物信息、医疗保健、自动驾驶和网络安全等,这样的误分行为将会导致严重的后果。对网络流量识别与分类技术以及分布外数据进行了简要介绍,提出了一种在测试样本中检测存在分布外数据的方法。根据分布外数据特点,通过训练并计算2个模型得到的结果的似然比判断分布外数据。在网络流量公开数据集Moore数据集和4个自采集数据集上进行了测试,该检测方法的识别精度可以达到92.3%。

Deep learning models sometimes misclassify some unknown categories of data into known categories.These unknown categories of data are defined as out-of-distribution data in some fields,such as biological information,medical care,automatic driving,network security and so on.These mistakes will lead to serious consequences.The identification and classification of network traffic and the out-of-distribution data are briefly introduced,and a method to detect the out-of-distribution data in test samples is proposed.According to the characteristics of out-of-distribution data,the out-of-distribution data can be judged by training two models and calculating the likelihood ratio of the results of the two models.The proposed method is tested on Moore data set and four self-collected data sets.The accuracy of the proposed method can reach 92.3%.