SECURITY AND AI LAB

Research Areas & Projects

Explore SAIL’s research areas, ongoing projects, and selected publications and preprints from 2026.

Malicious URL and Phishing Detection

This research area investigates methods for distinguishing legitimate web addresses from phishing, malware-related, and other malicious URLs. It examines URL representations, machine-learning approaches, feature efficiency, and the reliability of detection across different threat categories and datasets.

Journal Article 2026 A feature-efficient hierarchical classification strategy for malicious URL detection Özdaş M. B., Osmanoğlu M. Journal of Computer Virology and Hacking Techniques This study approaches malicious URL classification as a sequence of binary decisions rather than a single multiclass prediction. It combines lexical and structural URL features with contextual representations, examines feature-selection strategies, and compares classification models on benchmark datasets to assess performance with more compact feature sets.
Journal Article 2026 A feature-enriched deep learning based ensemble framework for robust phishing URL detection Kaya D., Osmanoğlu M. PeerJ Computer Science This study investigates phishing URL detection using enriched structural and lexical features. It compares traditional machine-learning techniques with deep-learning and ensemble models, including combinations of handcrafted URL features and sequence-based representations, to assess classification performance and the contribution of feature design.

CAPTCHA Security and Verification

This area studies the security and usability of CAPTCHA systems designed to distinguish human users from automated programs. Current research explores AI-generated visual challenges, human assessment of image suitability, automated evaluation of machine-solving difficulty, and feedback-driven improvements to challenge design.

Proposal in Development In development

A Human-in-the-Loop and Feedback-Driven Framework for Generative AI-Based Visual CAPTCHA Development and Evaluation

TÜBİTAK 1002-A

The proposed project develops and evaluates visual CAPTCHA challenges generated with AI. Human assessment examines image quality and semantic suitability, while automated evaluation considers the difficulty of machine-based solving. Feedback from these assessments is used to refine challenges with attention to both usability and security.

LLM-Generated Text Detection

This area studies methods for distinguishing human-written text from text generated by large language models. Research examines signals in language-model predictions, approaches that do not require detector-specific training, and the consistency of detection across different datasets, writing domains, and generator models.