Optimizing Bug Bounty Programs for Efficient Malware-Related Vulnerability Discovery

Abstract: Conventional security measures struggle to keep pace with the rapidly evolving threat of malware, which demands novel approaches for vulnerability discovery. Although Bug Bounty Programs (BBPs) are promising, they often underperform in attracting researchers, particularly in uncovering malware-related vulnerabilities. This study optimizes BBP structures to maximize engagement and target malware vulnerability discovery, ultimately strengthening cyber defense. Employing a mixed-methods approach, we compared public and private BBPs and analyzed the key factors influencing researcher participation and the types of vulnerabilities discovered. Our findings reveal a blueprint for effective malware-focused BBPs that enable targeted detection, faster patching, and broader software coverage. This empowers researchers and fosters collaboration within the cybersecurity community, significantly reducing the attack surface for malicious actors. However, challenges related to resource sustainability and legal complexity persist. By optimizing BBPs, we unlocked a powerful tool to fight cybercrime.

Semi Yulianto, Benfano Soewito, Ford Lumban Gaol and Aditya Kurniawan. “Optimizing Bug Bounty Programs for Efficient Malware-Related Vulnerability Discovery”. International Journal of Advanced Computer Science and Applications (IJACSA) 15.4 (2024). http://dx.doi.org/10.14569/IJACSA.2024.0150430

@article{Yulianto2024,
title = {Optimizing Bug Bounty Programs for Efficient Malware-Related Vulnerability Discovery},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150430},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150430},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {4},
author = {Semi Yulianto and Benfano Soewito and Ford Lumban Gaol and Aditya Kurniawan}
}