Ransomware detection using Deep Learning

Abstract

Ransomware is malicious software that encrypts victims' data and demands a ransom to decrypt them. This type of malware attacks are becoming more sophisticated, posing a significant threat to individuals and organizations. This research focuses on developing a powerful ransomware detection model that integrates behavioral analysis, deep learning, and bootstrapping techniques. The model uses behavioral analysis to identify ransomware samples, while deep learning techniques train multiple specialized models to detect zero-day ransomware attacks and minimize false positives. The proposed model outperforms machine learning algorithms in terms of accuracy, precision, and recall. This work should serve as the first step for further research and exploration of additional features, behavioral indicators, static analysis techniques, and hybrid approaches to enhance detection capabilities and combat ransomware threats, and finally to deployment in production. Keywords: Ransomware Detection, Deep Learning, Feedforward Neural Network, Machine Learning, Ensemble Learning.