DRMD: Deep Reinforcement Learning for Malware Detection under Concept Drift
PositiveArtificial Intelligence
- The paper presents a new method for malware detection using deep reinforcement learning (DRL) to address the challenges posed by evolving threats and limited labeling budgets. This approach allows for better adaptation to concept drift, which is crucial for maintaining effective malware detection systems.
- The development is significant as it enhances the ability of malware detection systems to remain effective in real
- While there are no directly related articles, the focus on DRL in malware detection aligns with ongoing discussions in the AI field about adaptive learning systems and their applications in cybersecurity.
— via World Pulse Now AI Editorial System