Ransomware Detection and Classification Using Machine Learning and Deep Learning

In the face of escalating ransomware threats, robust detection and classification methodologies are critical for safeguarding digital ecosystems. This study employs a comprehensive approach, combining advanced machine learning and deep learning (ML & DL) techniques, to enhance ransomware detection. Utilizing LSTM networks for deep learning and some methods such as Random Forest (RF), XGBoost, and LightGBM for machine learning-based classification, our models analyze subtle patterns indicative of ransomware behavior. By accurately classifying instances as benign or malicious, these models enable proactive defense measures. The results of this paper affirm the efficacy of our techniques and LSTM networks in enhancing ransomware detection and prediction capabilities, fortifying resilience against evolving cyber threats.