@Article{cmc.2026.084409,
AUTHOR = {Saeed Ullah, Junsheng Wu, Mian Muhammad Kamal, Mohammed K. Alzaylaee, Heba G. Mohamed},
TITLE = {Quantized Intrusion Detection for Resource-Constrained IoT: A Comparative Evaluation of Efficiency and Adversarial Robustness},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {},
YEAR = {},
NUMBER = {},
PAGES = {{pages}},
URL = {http://www.techscience.com/cmc/online/detail/27297},
ISSN = {1546-2226},
ABSTRACT = {The proliferation of Internet of Things (IoT) devices has introduced unprecedented security challenges, necessitating efficient intrusion detection systems (IDS) capable of operating under severe resource constraints. This research presents a hardware-informed empirical study of quantized neural-network-based intrusion detection for resource-constrained IoT platforms, using an ARM Cortex-M4 deployment target as a reference. We evaluate FP32, FP16, and INT8 TensorFlow Lite model variants derived from a lightweight 1D-CNN and assess their trade-offs in clean-data accuracy, model size, estimated inference latency, estimated energy consumption, and adversarial robustness. INT8-quantized model achieves 99.10% accuracy on clean data while maintaining 97.50% adversarial accuracy under Projected Gradient Descent (PGD) attacks with perturbation budget = 0.3. The quantized model achieves 12.0× latency reduction (0.083 vs. 0.995 ms) and 92.7% energy reduction (0.0083 vs. 0.1135 mJ) when compared to FP32. The memory footprint of the model is reduced by 55.7% from 58.02 to 25.72 KB. Our comprehensive analysis includes confusion matrices, ROC curves (AUC = 0.9964 for INT8), adversarial robustness heatmaps, and statistical significance testing via McNemar’s test. The results establish INT8 quantization as a viable solution for deploying robust IDS on resource-constrained IoT devices, achieving practical deployment feasibility without reducing detection performance.},
DOI = {10.32604/cmc.2026.084409}
}