Ransomware Attack

Ransomware attacks, characterized by malicious encryption of data for financial gain, pose a significant and evolving threat to individuals and organizations. Current research focuses on developing real-time detection methods using machine learning, particularly employing models like decision trees, multilayer perceptrons, convolutional neural networks, and XGBoost, often leveraging system call analysis, network traffic patterns, and process memory access to identify malicious activity. These efforts aim to improve the speed and accuracy of ransomware detection, enhance cybersecurity defenses, and mitigate the substantial economic and operational damage caused by these attacks. The development of robust and adaptable detection systems is crucial for effective ransomware mitigation.

Papers