A Proactive Approach for Behavior Based Ransomware Detection

Ransomware attacks pose a pervasive threat, demanding robust and proactive defense mechanisms. Traditional signature-based detection methods fall short in keeping pace with the evolving ransomware landscape. To tackle this issue, researchers have introduced a behavior-based ransomware detection tool. This tool employs various strategies, such as monitoring suspicious processes and behaviors, analyzing power consumption and CPU temperature, measuring changes in stored data entropy, and incorporating a honeypot environment. By effectively distinguishing between legitimate and ransomware-related activities, the tool takes a proactive approach to identify unknown or evolving ransomware strains. Continuous monitoring of power consumption and CPU temperature ensures precise detection of unusual encryption activities. Furthermore, entropy analysis adds an additional layer of detection by pinpointing alterations in stored data that may signal ransomware. The honeypot environment acts as a valuable resource for analyzing new ransomware variants and enhancing detection algorithms. This paper offers a comprehensive overview of the behavior-based ransomware detection tool, including its technical architecture, evaluation methods, and experimental results. The tool has demonstrated the ability to effectively detect ransomware attacks, safeguarding critical systems. It also suggests opportunities for further research and improvement in ransomware detection techniques.