Fileless malware attack detection based on multimodal large language models and verifiable reward reinforcement learning

Key Points

• The aim is to improve detection of fileless malware attacks using advanced multimodal models and reinforcement learning techniques.
• Utilized multimodal large language models for enhanced analysis.
• Applied verifiable reward reinforcement learning to optimize detection algorithms.
• Evaluated model performance against standard malware detection frameworks.
• Achieved a detection accuracy increase of 15% compared to traditional methods.
• Reduced false positive rates by 20%, enhancing reliability in real-time scenarios.
• Demonstrated adaptability in various cyber environments, indicating broad potential application.