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.
Opti- mization of rbf-svm kernel using grid search algorithm for ddos attack detection in sdn-based vanet,
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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.