3 papers
cs.CR2026
From Lab to Reality: A Practical Evaluation of Deep Learning Models and LLMs for Vulnerability Detection
Chaomeng Lu, Bert Lagaisse
Vulnerability detection methods based on deep learning (DL) have shown strong performance on benchmark datasets, yet their real-world effectiveness remains underexplored. Recent wo…
cs.CR2026
TMRugPull: A Temporally Sound Multimodal Dataset for Early RugPull Detection
Fatemeh Shoaei, Mohammad Pishdar, Mozafar Bag-Mohammadi +2
Rug pull is a critical attack in the world of blockchain technology. Despite this, the absence of sufficient time-bound and well-structured datasets is considered one of the signif…
cs.SE2025
ICVul: A Well-labeled C/C++ Vulnerability Dataset with Comprehensive Metadata and VCCs
Chaomeng Lu, Tianyu Li, Toon Dehaene +1
Machine learning-based software vulnerability detection requires high-quality datasets, which is essential for training effective models. To address challenges related to data labe…