Detecting Malware Using Dynamic Memory Heatmaps and Video Classification
Introduction The cybersecurity landscape is constantly evolving, with malware authors employing sophisticated techniques to evade detection by traditional antivirus (AV) solutions. As attackers leverage advanced methods to obfuscate and disguise malicious code, defenders must adopt innovative approaches to detect and mitigate threats effectively.
One such innovative approach is the use of dynamic memory heatmaps combined with video classification techniques for malware detection. By visualizing the memory usage patterns of executables during runtime and treating these patterns as frames in a video, we can leverage deep learning models designed for video classification to identify malicious behavior.
Leveraging Reinforcement Learning to Evade Dynamic Malware Detection
Introduction In the ever-evolving landscape of cybersecurity, dynamic malware detection systems play a crucial role in identifying and mitigating threats posed by malicious software. Traditional signature-based detection methods have become insufficient as malware authors adopt sophisticated techniques to obfuscate and morph their code, making detection increasingly challenging. Machine learning (ML) models have enhanced detection capabilities by analyzing behavioral patterns of executables during runtime. However, attackers are now leveraging advanced methods, such as reinforcement learning (RL), to automate the evasion of these dynamic detection systems.