Abstract
In this paper, we present a fast and accurate method for the classification of web content. Our algorithm uses the visual information of the main homepage saved in an image format by means of a full body snapshot. Sliding windows of different sizes and overlaps are used to obtain a large subset of images for each render. For each sub-image, a feature vector is extracted by means of a pre-trained deep learning model. A Extreme Learning Machine (ELM) model is trained for different values of hidden neurons using the large collection of features from a curated dataset of 5979 webpages with different classes: adult, alcohol, dating, gambling, shopping, tobacco and weapons. Our results show that the ELM classifier can be trained without the manual specific object tagging of the sub-images by giving excellent results in comparison to more complex deep learning models. A random forest classifier was trained for the specific class of weapons providing an accuracy of 95% with a F1 score of 0.8.
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Acknowledgments
The authors acknowledge the economical support by the Tekes (Business Finland) foundation via the project Cloud-assisted Security Services (CloSer). The authors would like also to thank their collaborators from F-Secure for sharing of the scientific research data. The authors wish to acknowledge CSC – IT Center for Science, Finland, for computational resources.
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Espinosa-Leal, L., Akusok, A., Lendasse, A., Björk, KM. (2021). Website Classification from Webpage Renders. In: Cao, J., Vong, C.M., Miche, Y., Lendasse, A. (eds) Proceedings of ELM2019. ELM 2019. Proceedings in Adaptation, Learning and Optimization, vol 14. Springer, Cham. https://doi.org/10.1007/978-3-030-58989-9_5
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