Classification of URL bitstreams using Bag of Bytes

TL;DR

This paper introduces a mechanical feature extraction method for classifying URL bitstreams, demonstrating improved accuracy over existing deep learning approaches using real-world and phishing data.

Contribution

The paper presents a novel mechanical approach for generating URL features that outperforms existing deep learning methods in URL classification accuracy.

Findings

Achieved 2-3% higher accuracy than existing DL-based methods.

Validated approach on real URL access data and phishing site data.

Demonstrated effectiveness of mechanical feature extraction in URL classification.

Abstract

Protecting users from accessing malicious web sites is one of the important management tasks for network operators. There are many open-source and commercial products to control web sites users can access. The most traditional approach is blacklist-based filtering. This mechanism is simple but not scalable, though there are some enhanced approaches utilizing fuzzy matching technologies. Other approaches try to use machine learning (ML) techniques by extracting features from URL strings. This approach can cover a wider area of Internet web sites, but finding good features requires deep knowledge of trends of web site design. Recently, another approach using deep learning (DL) has appeared. The DL approach will help to extract features automatically by investigating a lot of existing sample data. Using this technique, we can build a flexible filtering decision module by keep teaching the neural network module about recent trends, without any specific expert knowledge of the URL domain. In this paper, we apply a mechanical approach to generate feature vectors from URL strings. We implemented our approach and tested with realistic URL access history data taken from a research organization and data from the famous archive site of phishing site information, PhishTank.com. Our approach achieved 2~3% better accuracy compared to the existing DL-based approach.