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LLMs improve malware classification using multi-decompiler analysis · 2 sources tracked

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a method using Large Language Models (LLMs) to improve malware classification by analyzing decompiled code from multiple decompiler tools. The study found that combining decompiled views from Ghidra and RetDec enhances the F1 score for identifying malicious software, primarily by increasing the recall rate. This multi-decompiler approach offers a simple, training-free technique to boost the effectiveness of LLM-based malware triage in real-world scenarios. AI

IMPACT Enhances LLM capabilities in cybersecurity by improving malware detection accuracy through multi-view analysis.

RANK_REASON The cluster contains an academic paper detailing a new research methodology for LLM-based malware classification.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

LLMs improve malware classification using multi-decompiler analysis · 2 sources tracked

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The cluster contains an academic paper detailing a new research methodology for LLM-based malware classification.
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103 days old
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Bercan Turkmen, Vyas Raina ·

    Multi-View Decompilation for LLM-Based Malware Classification

    arXiv:2606.20436v1 Announce Type: cross Abstract: Malware analysts often inspect compiled binaries through decompiled pseudo-C, when source code is unavailable. Recent work suggests that large language models (LLMs) can assist this process by classifying decompiled code as benign…

  2. arXiv cs.AI TIER_1 English(EN) · Vyas Raina ·

    Multi-View Decompilation for LLM-Based Malware Classification

    Malware analysts often inspect compiled binaries through decompiled pseudo-C, when source code is unavailable. Recent work suggests that large language models (LLMs) can assist this process by classifying decompiled code as benign or malicious, but existing pipelines typically re…

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