Deep neural network models have been very successfully applied to Natural Language Processing (NLP) and Image based tasks. Their application to network analysis and management tasks is just recently being pursued. Our interest is in producing deep models that can be effectively generalized to perform well on multiple network tasks in different environments. A major challenge is that traditional deep models often rely on categorical features, but cannot handle unseen categorical values. One method for dealing with such problems is to learn contextual embeddings for categorical variables used by deep networks to improve their performance. In this paper, we adapt the NLP pre-training technique and associated deep model BERT to learn semantically meaningful numerical representations (embeddings) for Fully Qualified Domain Names (FQDNs) used in communication networks. We show through a series of experiments that such an approach can be used to generate models that maintain their effectiveness when applied to environments other than the one in which they were trained.
翻译:深神经网络模型被非常成功地应用于自然语言处理(NLP)和基于图像的任务。这些模型在网络分析和管理任务中的应用最近才刚刚开始。我们感兴趣的是制作能够有效推广的深层模型,以便在不同的环境中很好地完成多种网络任务。一个重大挑战是传统的深层模型往往依赖绝对特征,但无法处理不可见的绝对价值。处理这类问题的方法之一是学习深层网络用来改进其性能的绝对变量的背景嵌入。在本文中,我们调整了NLP培训前技术和相关的深层模型BERT, 以学习通信网络中使用的完全合格的域名(FQDNs)具有词义意义的数字表示(编组)。我们通过一系列实验显示,在应用到它们所培训的环境之外的环境时,可以使用这样一种方法来生成模型,以保持其有效性。