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A Deployment-Aware Feasibility Framework for Machine Learning-Based IoT Intrusion Detection Across Edge, Fog, and Cloud Architectures
The rapid growth and heterogeneity of Internet of Things (IoT) environments have exposed fundamental limitations in traditional rule-based and signature-based intrusion detection systems. This paper presents a quantitative deployment-aware analysis of machine learning (ML)-based intrusion detection
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