Timely identification of issue reports reflecting software vulnerabilities is crucial, particularly for Internet-of-Things (IoT) where analysis is slower than non-IoT systems. While Machine Learning (ML) and Large Language Models (LLMs) detect vulnerability-indicating issues in non-IoT systems, their IoT use remains unexplored. We are the first to tackle this problem by proposing two approaches: (1) combining ML and LLMs with Natural Language Processing (NLP) techniques to detect vulnerability-indicating issues of 21 Eclipse IoT projects and (2) fine-tuning a pre-trained BERT Masked Language Model (MLM) on 11,000 GitHub issues for classifying \vul. Our best performance belongs to a Support Vector Machine (SVM) trained on BERT NLP features, achieving an Area Under the receiver operator characteristic Curve (AUC) of 0.65. The fine-tuned BERT achieves 0.26 accuracy, emphasizing the importance of exposing all data during training. Our contributions set the stage for accurately detecting IoT vulnerabilities from issue reports, similar to non-IoT systems.
@article{arxiv.2511.01941,
title = {Detecting Vulnerabilities from Issue Reports for Internet-of-Things},
author = {Sogol Masoumzadeh},
journal= {arXiv preprint arXiv:2511.01941},
year = {2025}
}
Comments
ACCEPTED/To Appear in the Proceedings of the 40th IEEE/ACM International Conference on Automated Software Engineering (ASE) 2025. https://conf.researchr.org/details/ase-2025/ase-2025-student-research-competition/5/Detecting-Vulnerabilities-from-Issue-Reports-for-Internet-of-Things