This research introduces an advanced approach to detecting typosquatting attacks by leveraging large language models to identify deceptive URLs that traditional methods miss.

• Addresses evolving typosquatting techniques that exploit human typing errors to conduct phishing and distribute malware
• Demonstrates how LLMs can identify sophisticated domain impersonation beyond conventional pattern-based detection
• Provides a scalable solution for security teams as domain threats multiply with new TLDs
• Serves as a critical defensive tool against threats targeting individuals, organizations, and national cybersecurity infrastructure

Business Impact: As online presence becomes increasingly crucial, this approach offers enhanced protection for brand reputation, customer trust, and digital assets against increasingly sophisticated domain-based attacks.

Training Large Language Models for Advanced Typosquatting Detection