Ransomware Detection Using Deep Q-Network and L2PGD Attack Analysis on a Custom Dataset

In the current fast changing cyberspace, ransomware has continued to be a formidable threat. Through this research, using deep reinforcement learning and adversarial attack models, we undertook performance analysis evaluation of a locally constructed ransomware dataset. The dataset contained key dynamic features that were extracted from raw ransomware samples processed in Cuckoo sandbox environment. Our approach combined supervised learning for initial detection and Deep Q-Network (DQN) algorithm for adaptive behavioral analysis. An L2 Projected Gradient Descent (L2PGD) adversarial attack was then carried out to evaluate the robustness of both security and stability of the ransomware detection model. The results that were obtained demonstrated that Deep Reinforcement Learning (DRL) can effectively classify samples as benign and ransomware. Moreover, the successful adversarial attack underscores the need for improved robustness measures in artificial intelligence models.