Credentials Stuffing Attack Prevention Using Machine Learning

Millions of compromised databases are traded online daily, allowing hackers to exploit users’ accounts and posing a significant threat to online security by jeopardizing individuals’ sensitive information. There is an urgent need for enhanced cybersecurity measures and increased public awareness to mitigate these risks. This study highlights the significant concern of account cracking stuffing attacks, a prevalent cybersecurity threat that endangers online systems. Our research implemented machine learning methodologies on web access logs to develop a proactive approach for detecting and preventing unauthorized access attempts. We propose a novel solution that utilizes machine learning on web access logs to enhance account security dynamically and adaptively. By effectively using the DBSCAN clustering algorithm to detect discrete user behavior clusters, we categorize…