Reinforcing cybersecurity with Bloom filters: a novel approach to password cracking efficiency

Abstract

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.