Security issues on Forensics Applications by Dynamic Malware injection – A Review
E. Yuvarani, P.M. Gomathi · 2024
Data security and privacy have become paramount concerns in today’s digital age. Malware attacks pose significant threats to individuals and organizations alike. This research paper investigates the application of machine learning techniques to detect and prevent malware attacks. The study explores two primary approaches to malware detection: static analysis and dynamic analysis. Static analysis examines the code without executing it, while dynamic analysis involves monitoring the behavior of the code during execution. By combining these techniques, a more comprehensive and effective approach to malware detection can be achieved. This research study reviews existing research on malware detection and discusses the potential of machine learning algorithms to improve detection accuracy. Future research directions, such as incorporating advanced deep learning techniques and leveraging behavioral analysis, are explored to further enhance the resilience of systems against malware attacks.