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.
An efficient self attention-based 1d-cnn-lstm network for iot attack detection and identi- fication using network traffic
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
A SE-enhanced ViT-BiLSTM hybrid model reports 99.33% accuracy on EdgeIIoT and 98.16% on CICIoMT2024 for intrusion detection after data balancing.
citing papers explorer
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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.
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SE-Enhanced ViT and BiLSTM-Based Intrusion Detection for Secure IIoT and IoMT Environments
A SE-enhanced ViT-BiLSTM hybrid model reports 99.33% accuracy on EdgeIIoT and 98.16% on CICIoMT2024 for intrusion detection after data balancing.