A Lightweight Deep Learning Framework for Real-Time Cyberattack Detection in Internet of Things Networks

The rapid expansion of Internet of Things (IoT) networks introduces significant cybersecurity challenges because connected sensors, gateways, and embedded controllers operate with limited memory, processing capacity, and energy. Conventional intrusion detection solutions may impose computational costs that exceed the capabilities of these devices, while cloud-only analysis adds latency and creates privacy concerns. This narrative review develops a conceptual lightweight deep learning framework for real-time cyberattack detection at the IoT edge. The proposed architecture combines flow-based feature extraction, selective preprocessing, a compact one-dimensional convolutional network, calibrated risk scoring, and adaptive response at the gateway. It discusses feature selection, class imbalance, model compression, data drift, adversarial resilience, and deployment constraints. An illustrative design chart compares qualitative trade-offs between model classes; it does not represent measured experimental findings. The article outlines an evaluation protocol using reproducible train-test separation, device-aware latency measurement, and cross-dataset validation. The central conclusion is that practical reliability requires jointly optimizing detection quality, false-alarm burden, inference time, memory use, and operational maintainability rather than accuracy alone.