AI-Based Homoglyph detection and behavirol profiling for phishing prevention

Author(s)	Ms. Sonica B K, Mr. Shri Rajendra Birje, Mr. Tanzil Ahamad, Mr. Shanth Kumar, Prof. Vinutha G K
Country	India
Abstract	Phishing remains a major cybersecurity threat, with homoglyph attacks—using visually similar Unicode characters to mimic legitimate domains—becoming increasingly sophisticated. These attacks often bypass traditional detection systems, which rely on string matching and blacklists. This paper proposes a hybrid deep learning and NLP-based framework to detect homoglyph-based phishing by analyzing both the visual and semantic structure of domain names. We use convolutional and transformer models for character-level analysis and incorporate behavioral profiling—such as keystroke dynamics and navigation patterns—to identify anomalies. We review current detection methods and highlight their limitations, especially in multilingual and cross-device scenarios. Our system is scalable, context-aware, and capable of real-time detection, achieving over 94% precision while reducing false positives by up to 40%.
Keywords	(1) analysis of AI-based homoglyph detection, (2) integration of behavioral analytics, (3) a unified detection framework, and (4) discussion of deployment challenges like privacy and latency. This work emphasizes combining technical and behavioral insights for adaptive phishing defense
Field	Engineering
Published In	Volume 16, Issue 4, October-December 2025
Published On	2025-11-10

About IJSAT Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us	Message on WhatsApp	+91-9687-182-185	editor@ijsat.org

International Journal on Science and Technology