Adversarial Epistemic Load Amplification Through Uncertainty Inflation and Selective Escalation Saturation in Edge–Cloud IoT Intrusion Detection

Uncertainty-aware edge–cloud intrusion detection improves analytical efficiency by escalating ambiguous Industrial Internet of Things (IIoT) traffic for deeper cloud inspection, but this mechanism can itself become an attack surface when uncertainty is deliberately manipulated. This study formulates Adversarial Epistemic Load Amplification (AELA), a multi-stage attack in which protocol-valid and operationally plausible observations are optimized to increase epistemic uncertainty, selective escalation, cloud utilization, queue pressure, and analysis delay without relying on direct classifier evasion. Ten AELA variants are defined across uncertainty intensity, injection rate, stealth, persistence, and targeted intrusion activation. To counter this threat, an Epistemic Resilience Framework combines an Epistemic Escalation Risk Score (EERS) with an Epistemic Load Amplification Score (ELAS) and applies adaptive uncertainty gating, capacity-aware admission, source token control, redundancy suppression, local deep inspection, priority escalation, and closed-loop recovery. Experiments on the X-IIoTID dataset show 98.74% baseline accuracy and 0.996 AUROC. Under the strongest AELA condition, uncertainty increases by 98.5%, 78.6% of observations are escalated, peak cloud utilization reaches 1.46, queue pressure reaches 2.31, normalized delay reaches 2.07, and targeted intrusion success reaches 88.7%. The proposed EERS–ELAS defense detects 96.8% of AELA activity, reduces escalation by 57.9%, prevents 94.7% of saturation events, preserves 98.1% priority threat recall, and reduces targeted intrusion success to 9.4%. These findings show uncertainty must be governed with analytical capacity to preserve resilient edge–cloud intrusion detection under adversarial pressure.