Dynamic Ransomware Detection Using Time-Based Api Call Analysis

Abstract

Dynamic Ransomware Detection Using Time-Based API Call Analysis is a cybersecurity-based project developed to detect ransomware attacks in real time. The project monitors API calls generated by applications and analyses their timing behavior to identify malicious activities. Machine learning techniques are used to classify whether the behaviour is normal or malicious. Dynamic Ransomware Detection Using Time-Based API Call Analysis is a cybersecurity- based project developed to detect ransomware attacks in real time. Ransomware is a type of malicious software that encrypts user files and demands payment for recovery. Traditional antivirus systems often fail to detect newly emerging ransomware variants. This project focuses on monitoring API calls generated by applications and analysing their timing behavior. By studying the sequence and time intervals of API calls, the system can identify suspicious activities related to ransomware attacks. Machine learning techniques are used to classify whether the behavior is normal or malicious. The proposed system helps in early ransomware detection before severe damage occurs, thereby improving system security and reducing data loss.