A Heuristic Approach for Website Classification with Mixed Feature Extractors

摘要

We proposed an intelligent website classification schema based on deep neural networks using mixed featured extractors. With the guidance of supervised learning methods and iterative training, we use the gradient descent algorithm to model the website classification. This novel model is composed of four components, which includes a Website Encoder, a Text CNN Feature Extractor, a Bidirectional GRU Feature Extractor and a Fully Connected Classifier. It can extract multiple features at different granularities of a website. By using the concatenated mixed features taken from mixed feature extractors, our model can easily choose a suitable website class. We make extensive experiments on the realistic collected website dataset. The dataset is collected using domains extracted from DNS records of Telecom Operator. Compared the multiple widely used machine learning models and our novel model, results demonstrate the proposed classification schema outperforms the current models with the metrics precision, recall, F1, and accuracy. All of this can contribute to various web applications, such as malicious website detection, online advertising, etc.