Safeguarding digital information against unauthorized access is critical in the industry context. Techniques for cracking passwords are essential tools for both attackers and defenders. This study explores the utilization of Bloom filters, a probabilistic data structure known for its space and time efficiency, to refine the password-cracking process. We demonstrate that by employing Bloom filters, it is possible to significantly enhance the performance of password-cracking techniques in terms of speed and memory consumption. By conducting a comparative analysis with prevalent techniques such as hash tables and binary search, we demonstrate the superior performance of Bloom filters. The experimentation, utilizing a publicly available dataset of leaked password hashes, indicates a significant improvement in cracking efficiency. The findings contribute to the broader cybersecurity goal of developing resilient systems against password-related breaches, underscoring the importance of integrating cutting-edge research and practical applications to fortify digital defenses.

Abstract Cyberthreats continue their expansion, becoming more and more complex and varied. However, credentials and passwords are still a critical point in security. Password cracking can be a powerful tool to fight against cyber criminals if used by cybersecurity professionals and red teams, for instance, to evaluate compliance with security policies or in forensic investigations. For particular systems, one crucial step in the password-cracking process is comparison or matchmaking between password-guess hashes and real hashes. We hypothesize that using newer data structures such as Cuckoo filters could optimize this process. Experimental results show that, with a proper configuration, this data structure is two orders of magnitude more efficient in terms of size/usage compared to other data structures while keeping a comparable performance in terms of time.