All Outputs (2)

Vulnerability detection through machine learning-based fuzzing: A systematic review (2024)
Journal Article
Bamohabbat Chafjiri, S., Legg, P., Hong, J., & Tsompanas, M. A. (2024). Vulnerability detection through machine learning-based fuzzing: A systematic review. Computers and Security, 143, Article 103903. https://doi.org/10.1016/j.cose.2024.103903

Modern software and networks underpin our digital society, yet the rapid growth of vulnerabilities that are uncovered within these threaten our cyber security posture. Addressing these issues at scale requires automated proactive approaches that can... Read More about Vulnerability detection through machine learning-based fuzzing: A systematic review.

Improving search space analysis of fuzzing mutators using cryptographic structures (2023)
Conference Proceeding
Chafjiri, S. B., Legg, P., Tsompanas, M., & Hong, J. (in press). Improving search space analysis of fuzzing mutators using cryptographic structures. In Lecture Notes in Network Security

This paper introduces a novel approach to enhance the performance of software fuzzing mutator tools, by leveraging cryptographic structures known as substitution-permutation networks and Feistel networks. By integrating these structures into the exis... Read More about Improving search space analysis of fuzzing mutators using cryptographic structures.