Ransomware Attack Modeling Technique
Ransomware attack modeling focuses on developing techniques to simulate, detect, and mitigate ransomware attacks, primarily to improve cybersecurity defenses. Current research emphasizes using machine learning, particularly deep learning models like Convolutional Neural Networks (CNNs) and reinforcement learning (RL), along with techniques like Natural Language Processing (NLP) and eBPF for real-time analysis of system behavior and network traffic. These models are applied to various data sources, including process memory, API calls, and network traffic patterns, to achieve high accuracy in ransomware detection and to understand attacker strategies. This research directly impacts cybersecurity practices by informing the development of more effective detection and prevention tools, as well as providing insights into the evolving tactics of ransomware operators.