Ransomware Detection using Machine Learning Models Includes Source Code, PPT, Synopsis, Report, Documents, Base Research Paper & Video tutorials
Implementaion Video: https://youtu.be/q-zCbLd1Geo?si=Hr_dJuJfCb5Qxhq0
Ransomware attacks have become a significant threat to computer systems, causing data loss and financial damage. To combat this, we propose a ransomware detection system using machine learning models. Our approach utilizes a combination of static and dynamic features to identify ransomware behavior. Machine Learning Algorithms such as Random Forest, Support Vector Machine, and Convolutional Neural Networks are employed to classify malware samples as ransomware or benign. The system is trained on a large dataset of labeled samples, allowing it to learn patterns and anomalies in ransomware code. Key Features include file system analysis, network traffic monitoring, and system call tracking. The model achieves high accuracy in detecting ransomware, with a Detection Rate of 95% and a False Positive Rate of 2%. Our system provides a robust and effective solution for detecting and preventing ransomware attacks, protecting sensitive data and preventing financial losses.
Ransomware Detection, Machine Learning, Artificial Intelligence, Cyber Security, Malware Analysis, Threat Detection, Deep Learning, Data Protection, Network Security, System Call Analysis, File System Monitoring
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