Introduces two carbon-aware DRL-based intrusion detection systems for IoT edge gateways, reporting 94% accuracy for a supervised LSTM-DRL model and 98% for a label-free Autoencoder-DRL hybrid.
Machine learning in real-time internet of things (iot) systems: A survey
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Carbon-Aware Intrusion Detection: A Comparative Study of Supervised and Unsupervised DRL for Sustainable IoT Edge Gateways
Introduces two carbon-aware DRL-based intrusion detection systems for IoT edge gateways, reporting 94% accuracy for a supervised LSTM-DRL model and 98% for a label-free Autoencoder-DRL hybrid.