AI RESEARCH
Explainable Threat Attribution for IoT Networks Using Conditional SHAP and Flow Behavior Modelling
arXiv CS.LG
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ArXi:2603.22771v1 Announce Type: cross As the Internet of Things (IoT) continues to expand across critical infrastructure, smart environments, and consumer devices, securing them against cyber threats has become increasingly vital. Traditional intrusion detection models often treat IoT threats as binary classification problems or rely on opaque models, thereby limiting trust. This work studies multiclass threat attribution in IoT environments using the CICIoT2023 dataset, grouping over 30 attack variants into 8 semantically meaningful classes.