Authors: Mohamed Amine Ferrag, Mthandazo Ndhlovu, Norbert Tihanyi, Lucas C. Cordeiro, Merouane Debbah, Thierry Lestable, Narinderjit Singh Thandi

Published on: June 25, 2023

Impact Score: 8.52

Arxiv code: Arxiv:2306.14263

Summary

• What is new: The introduction of SecurityBERT, utilizing BERT for cybersecurity in IoT networks with a special focus on privacy-preserving techniques.
• Why this is important: The rise in frequency and diversity of cybersecurity attacks, especially in expanding IoT networks, necessitates efficient incident detection.
• What the research proposes: SecurityBERT leverages BERT and a novel privacy-preserving encoding to effectively detect cyber threats in IoT networks.
• Results: SecurityBERT achieved an impressive 98.2% accuracy in identifying fourteen distinct attack types, with minimal inference time and compact model size.

Technical Details

Technological frameworks used: Transformer architectures, specifically BERT.

Models used: SecurityBERT with Privacy-Preserving Fixed-Length Encoding (PPFLE) and Byte-level Byte-Pair Encoder (BBPE) Tokenizer.

Data used: Edge-IIoTset cybersecurity dataset.

Potential Impact

Cybersecurity firms, IoT device manufacturers, network security services, and companies specializing in AI for cybersecurity.

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