Trident combines static decision trees, LLM-generated behavioral rules from sandbox reports, and direct LLM analysis via majority voting to outperform static methods while resisting concept drift without retraining.
Sorel-20m: A large scale benchmark dataset for malicious pe detection
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LCC-LLM creates a code-centric dataset and RAG-based LLM framework that reaches 0.634 average semantic similarity on 43 malware tasks and 10/10 pass rate in real-world case studies.
Cybersecurity's scale, adversaries, labeling issues, and operational demands make it the superior test-case for general AI progress over NLP or computer vision.
citing papers explorer
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Trident: Improving Malware Detection with LLMs and Behavioral Features
Trident combines static decision trees, LLM-generated behavioral rules from sandbox reports, and direct LLM analysis via majority voting to outperform static methods while resisting concept drift without retraining.
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LCC-LLM: Leveraging Code-Centric Large Language Models for Malware Attribution
LCC-LLM creates a code-centric dataset and RAG-based LLM framework that reaches 0.634 average semantic similarity on 43 malware tasks and 10/10 pass rate in real-world case studies.
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Cybersecurity is the True Frontier for Generative AI Success or Failure
Cybersecurity's scale, adversaries, labeling issues, and operational demands make it the superior test-case for general AI progress over NLP or computer vision.