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A novel hybrid TabTransformer–LightGBM framework for explainable intrusion detection in smart farming IoT networks
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DOI：10.1016/j.compeleceng.2026.111344.png)
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Abstract
En 中文
• Smart farming suffers significant cybersecurity challenges. • Traditional IDSs fails in generalizing across diverse farms or providing interpretable alerts. • We propose a framework with XAI for scalable, near real-time intrusion diagnosis. • Our framework enables the detection of volumetric anomalies, spoofing, and service misuse. • Our framework offers an efficient and scalable solution for securing smart farming IoT networks.
Keywords:
Cybersecurity
Machine learning
Anomaly detection
Cloud computing
IoT analytics
Precision agriculture
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