MARD is a ReAct-based multi-agent LLM framework that detects Android malware at 93.46% F1 without fine-tuning and shows robustness to concept drift across five-year evaluations.
Dmalnet: Dynamic malware analysis based on api feature engineering and graph learning.Computers & Security, 122:102872, 2022
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ABLE uses LLMs with sanitization and iterative refinement to generate bypass YARA rules from malware traces, achieving 79% success on 334 samples and 47% more family detections.
citing papers explorer
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MARD: A Multi-Agent Framework for Robust Android Malware Detection
MARD is a ReAct-based multi-agent LLM framework that detects Android malware at 93.46% F1 without fine-tuning and shows robustness to concept drift across five-year evaluations.
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A Large Language Model Approach to Generating Bypass Rules for Malware Evasion in Analysis Sandbox
ABLE uses LLMs with sanitization and iterative refinement to generate bypass YARA rules from malware traces, achieving 79% success on 334 samples and 47% more family detections.