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.
LLMs for Cyber Security: New Opportunities
1 Pith paper cite this work. Polarity classification is still indexing.
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Pith paper citing it
abstract
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.
fields
cs.CR 1years
2025 1verdicts
REJECT 1representative citing papers
citing papers explorer
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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.