Anticipating Threats through Malware Detection Approaches to safeguard Data Privacy and Security: An In-Depth Study

Although automated security tools and security analysts currently manage to handle approximately 80% of cyber threats on a global scale, the remaining 20% poses a significant risk. Among the most prevalent threats, malware stands out as a particularly concerning one due to its self-propagating nature and the unresolved vulnerabilities are exploited by malicious actors. In this study, various approaches are explored to combat malware, ranging from static and dynamic analysis techniques to signature-based, heuristic-based, behavior-based, model checking-based, cloud-based, machine learning and deep learning-based, mobile device-based, and IoT-based approaches. By conducting a comprehensive comparison and analysis of these methodologies, we aim to identify the techniques that can be proved to be most effective to mitigate the risks associated with various malwares. The findings and results obtained from this study contribute to the development of more robust strategies for maintaining a secure cyber environment.