LSTM network identifies IoT devices with 80% accuracy

Researchers have developed a machine learning pipeline to identify IoT devices using Long Short-Term Memory (LSTM) networks. The system processes raw network packet captures into engineered features, which are then fed into the LSTM model as time-series sequences. The model achieved an accuracy of 79.85% and a macro-averaged F1-score of 75.70% across 27 device classes, with optimal performance observed at a sequence length of 18. AI

IMPACT Enhances IoT security by providing a more accurate method for device identification and vulnerability detection.

RANK_REASON Academic paper detailing a novel machine learning approach for IoT device identification. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• Aalto university dataset
• IoT device identification
• Long Short-Term Memory

• paper
• product

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