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Found 4 papers, 2 papers with code
ZEST: Attention-based Zero-Shot Learning for Unseen IoT Device Classification
Recent research works have proposed machine learning models for classifying IoT devices connected to a network.
Markov Chain Monte Carlo-Based Machine Unlearning: Unlearning What Needs to be Forgotten
Similarly, MCU can be used to erase the lineage of a user's personal data from trained ML models, thus upholding a user's "right to be forgotten".
Cost-aware Feature Selection for IoT Device Classification
In this work, we take a more realistic approach, and argue that feature extraction has a cost, and the costs are different for different features.
GEE: A Gradient-based Explainable Variational Autoencoder for Network Anomaly Detection
This paper looks into the problem of detecting network anomalies by analyzing NetFlow records.
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