Hybrid Phishing URL Detection Using Segmented Word Embedding

Abstract

Phishing is a type of cybercrime committed by attackers to steal sensitive information. This paper focuses on URL-based phishing detection, i.e., detecting phishing webpages by analyzing the URL. Previously proposed methods tackled this problem; however, insufficient word tokenization of URLs arises unknown words, which degrades the detection accuracy. To solve the unknown-word problem, we propose a new tokenization algorithm, called URL-Tokenizer, which integrates BERT and WordSegment tokenizers, besides utilizing 24 NLP features. Then, we adopt the URL-Tokenizer to the DNN-CNN hybrid model to leverage the detection accuracy. Our experiment using the Ebbu2017 dataset confirmed that our word-DNN-CNN achieves an AUC of 99.89% compared to the state-of-the-art DNN-BiLSTM with an AUC of 98.78%.