Multitask fine-tuning of an encoder-decoder model on prompted datasets produces zero-shot generalization that often beats models up to 16 times larger on standard benchmarks.
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ESPRESSO achieves over 0.99 true positive rate at 10^{-3} false positive rate for stepping-stone intrusion detection on synthetic data for SSH, SOCAT, ICMP, DNS and mixed protocols, outperforming DeepCoFFEA while also enabling chain length prediction.
RedShell fine-tunes LLMs on enhanced malicious PowerShell data to produce syntactically valid offensive code for pentesting, reporting over 90% validity, strong semantic match to references, and better edit-distance similarity than prior methods plus functional execution success.
Hybrid DEM-ARIMA-ML framework accelerates transient particle simulations while claiming to retain accuracy and match literature results.
citing papers explorer
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Multitask Prompted Training Enables Zero-Shot Task Generalization
Multitask fine-tuning of an encoder-decoder model on prompted datasets produces zero-shot generalization that often beats models up to 16 times larger on standard benchmarks.
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Tracing the Chain: Deep Learning for Stepping-Stone Intrusion Detection
ESPRESSO achieves over 0.99 true positive rate at 10^{-3} false positive rate for stepping-stone intrusion detection on synthetic data for SSH, SOCAT, ICMP, DNS and mixed protocols, outperforming DeepCoFFEA while also enabling chain length prediction.
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Towards Automated Pentesting with Large Language Models
RedShell fine-tunes LLMs on enhanced malicious PowerShell data to produce syntactically valid offensive code for pentesting, reporting over 90% validity, strong semantic match to references, and better edit-distance similarity than prior methods plus functional execution success.
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A machine learning framework for computationally expensive transient models
Hybrid DEM-ARIMA-ML framework accelerates transient particle simulations while claiming to retain accuracy and match literature results.