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LLMs for Cyber Security: New Opportunities
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Large language models (LLMs) are a class of powerful and versatile models that are beneficial to many industries. With the emergence of LLMs, we take a fresh look at cyber security, specifically exploring and summarizing the potential of LLMs in addressing challenging problems in the security and safety domains.
Forward citations
Cited by 3 Pith papers
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Data-Efficient Adaptation of LLMs via Attention Head Reweighting
Learning a single scalar per attention head lets LLMs adapt to few-shot text classification better than LoRA, with 200–1000x fewer trainable parameters.
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Large Language Models in Cybersecurity: Applications, Vulnerabilities, and Defense Techniques
A survey that maps LLM applications, vulnerabilities, and defenses across eight cybersecurity domains, but with significant citation and rigor problems.
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LLM-Based Threat Detection and Prevention Framework for IoT Ecosystems
Fine-tuned BERT-small reaches 99.75% test accuracy on combined IoT-23 and TON IoT attack classification, but the claimed real-time prevention advantage over traditional methods is not demonstrated.
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