[2601.20346] Multimodal Multi-Agent Ransomware Analysis Using AutoGen

Computer Science > Cryptography and Security arXiv:2601.20346 (cs) [Submitted on 28 Jan 2026 (v1), last revised 3 Mar 2026 (this version, v2)] Title:Multimodal Multi-Agent Ransomware Analysis Using AutoGen Authors:Asifullah Khan, Aimen Wadood, Mubashar Iqbal, Umme Zahoora View a PDF of the paper titled Multimodal Multi-Agent Ransomware Analysis Using AutoGen, by Asifullah Khan and 3 other authors View PDF Abstract:Ransomware has become one of the most serious cybersecurity threats causing major financial losses and operational disruptions this http URL detection methods such as static analysis, heuristic scanning and behavioral analysis often fall short when used alone. To address these limitations, this paper presents multimodal multi agent ransomware analysis framework designed for ransomware classification. Proposed multimodal multiagent architecture combines information from static, dynamic and network sources. Each data type is handled by specialized agent that uses auto encoder based feature extraction. These representations are then integrated through a fusion agent. After that fused representation are used by transformer based classifier. It identifies the specific ransomware family. The agents interact through an interagent feedback mechanism that iteratively refines feature representations by suppressing low confidence information. The framework was evaluated on large scale datasets containing thousands of ransomware and benign samples. Multiple experiments were ...