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Neuro-immune transformer with contextual self-attention for IoT intrusion detection
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Summary
Researchers propose the Neuro-Immune Transformer (NIT), a hybrid intrusion detection architecture that pairs a transformer-based contextualization engine with an adaptive Artificial Immune System using a Clonal Selection Algorithm for detector evolution. Evaluated on the imbalanced WUSTL EHMS 2020 IoMT dataset, NIT reports 98.04% accuracy, a 92.9% detection rate, a 0.9168 F1-score and a 1.4% false alarm rate, and it resists Gaussian noise and FGSM adversarial attacks. With 856 K parameters and 3.2 ms inference latency, it targets resource-constrained edge-IoT deployment in healthcare networks.