A real-world multi-modal Wi-Fi fault dataset and unified benchmark are introduced to evaluate diagnosis approaches across tasks, modalities, and LLM-based reasoning.
Towards the development of realistic botnet dataset in the internet of things for network forensic analytics: Bot-iot dataset
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
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.
citing papers explorer
-
Toward Realistic Wi-Fi Fault Diagnosis: A Multi-Modal Benchmark
A real-world multi-modal Wi-Fi fault dataset and unified benchmark are introduced to evaluate diagnosis approaches across tasks, modalities, and LLM-based reasoning.
-
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.