Detection of Phishing URLs Using Temporal Convolutional Network

Abstract

Throughout the past few years, phishing attacks have become an increasingly substantial problem for individuals and organizations. In this non-technical attack, the victim is deceived into accessing a malicious URL that downloads a malicious program to access the network or redirects the victim to a page that requests sensitive information. The literature is filled with many research proposals to mitigate this problem however, the dynamic nature and the creativity of the attacker have made it difficult and reoccurring. In this paper, we proposed to use novel deep learning techniques, namely the Temporal convolutional network (TCN) with word embedding, to detect phishing URLs. As a result of our experiments, we found that our model can detect phishing URLs with 98.95% accuracy, 98% precision, 98% recall, and 98% f1-score. This result indicates that our model is effective against phishing attacks.