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Multimodal Multi-Agent Ransomware Analysis Using AutoGen

arXiv – CS AI|Asifullah Khan, Aimen Wadood, Mubashar Iqbal, Umme Zahoora|March 4, 2026 at 05:00 AM|1 views

🤖AI Summary

Researchers developed a multimodal multi-agent ransomware analysis framework using AutoGen that combines static, dynamic, and network data sources for improved ransomware detection. The system achieved 0.936 Macro-F1 score for family classification and demonstrated stable convergence over 100 epochs with a final composite score of 0.88.

Key Takeaways

→New multimodal framework combines static, dynamic, and network analysis using specialized AI agents with autoencoder-based feature extraction.
→System outperforms single-modality baselines achieving 0.936 Macro-F1 score for ransomware family classification.
→Interagent feedback mechanism iteratively refines feature representations by suppressing low-confidence information.
→Framework demonstrates stable monotonic convergence over 100 epochs with +0.75 absolute improvement in agent quality.
→Confidence-aware abstention feature enables reliable real-world deployment by avoiding forced classifications.

#cybersecurity #ransomware #multi-agent #ai-detection #autogen #transformer #machine-learning #security-research

Read Original →via arXiv – CS AI

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