Publication Date
2025
Document Type
Thesis
Committee Members
Junjie Zhang, Ph.D. (Advisor); Krishnaprasad Thirunarayan, Ph.D. (Committee Member); Lingwei Chen, Ph.D. (Committee Member)
Degree Name
Master of Science in Cyber Security (M.S.C.S.)
Abstract
Software vulnerabilities are a major cause of security breaches, making effective detection critical. Traditional learning-based methods require large datasets and significant computational resources, which are often impractical due to high annotation costs and data scarcity. To address this, we propose an innovative system, RearVul, which Re-parameterizes adversarial reprogramming in a low-dimensional subspace for software vulnerability detection. Unlike conventional approaches, RearVul repurposes a pre-trained classification model using adversarial reprogramming, enabling detection with minimal modifications. It learns a universal perturbation applied to program representations, preserving the original model’s feature extraction capabilities while adapting it to a new domain. Furthermore, we introduce a low-dimensional re-parameterization strategy that optimizes rank decomposition tensors of the perturbation, significantly reducing the number of trainable parameters and computational overhead. Extensive evaluations on public vulnerability datasets demonstrate that RearVul achieves competitive detection accuracy while drastically improving efficiency. Notably, in data-limited scenarios where conventional training methods struggle, RearVul consistently outperforms existing solutions, highlighting its adaptability and effectiveness for real-world software vulnerability detection.
Page Count
53
Department or Program
Department of Computer Science and Engineering
Year Degree Awarded
2025
Copyright
Copyright 2025, all rights reserved. My ETD will be available under the "Fair Use" terms of copyright law.
ORCID ID
0000-0003-0344-4494
