Transformer-Based Model for Malicious URL Classification

• Main Authors: Do N.Q., Selamat A., Lim K.C., Krejcar O., Ghani N.A.M.
• Other Authors: 57283917100
• Format: Conference Paper
• Published: Institute of Electrical and Electronics Engineers Inc. 2024
• Subjects: malicious URL classification; natural language processing; phishing detection; transformer model; unsupervised learning; Classification (of information); Computer crime; Learning algorithms; Learning systems; Natural language processing systems; Supervised learning; Viruses; Zero-day attack; 'current; Cyber threats; Labeled data; Language processing; Malicious uniform resource locator classification; Natural language processing; Natural languages; Phishing; Phishing detections; Transformer modeling; Deep learning
• Institution: Universiti Tenaga Nasional

In recent years, cyber threats including malicious software, virus, spam, and phishing have grown aggressively via compromised Uniform Resource Locators (URLs). However, the current phishing URL detection solutions based on supervised learning use labeled data for training and classification, leadin...