With the increasing sophistication and frequency of cyber threats, cybersecurity and reverse engineering have become critical areas of research. The advent of big data and machine learning techniques has provided new opportunities to enhance security measures and counter malicious activities. This research article presents a comprehensive analysis of databases and machine learning methods employed in the domains of cybersecurity and reverse engineering. We examine various databases used for storing and managing security-related data, including threat intelligence, malware samples, and network traffic logs. Additionally, we explore the application of machine learning algorithms for anomaly detection, intrusion detection, malware analysis, and vulnerability assessment. The study evaluates the strengths, limitations, and challenges associated with different databases and machine learning techniques, along with their implications for effective cybersecurity and reverse engineering practices. Furthermore, we highlight potential future research directions in this field.

cybersecurity, reverse engineering, machine learning, database