Rare?Attack?Aware Hybrid Deep Learning for IoT Intrusion Detection

Intrusion detection in Internet?of?Things (IoT) networks is a balancing act: We need detectors that work within the limits of edge devices. Still catch the small signs of rare attacks. Paper presents a design that we plan to build and test. Our method uses an elastic deep autoencoder (EDA) and adds three ideas guided by human expertise: contrastive pretraining to help separate subtle classes, focal-loss supervision to focus on areas with limited data, few-shot meta-learning to handle low-support or new attacks. I explain why I chose each part, how they work together and how I will test them in realistic conditions with limited resources. Of results I provide expected outcomes and a clear plan; the focus is on the approach reasoning and a reproducible plan that can be implemented. I am using an EDA at the core of our approach as its compact and elastic; suitable for networks and help us to notice the signals that rare attacks use. The Internet of Things (IoT) networks have edge constraints. I want to respect the edge constraints of IoT networks. Rare attacks leave behind signals. I plan to build and evaluate our approach. I will use a design which is rarity-aware, support emerging attacks. I will use fewshot meta-learning helping us to cope with support or emerging attacks. I will use focal-loss supervision to put attention where data is scarce. I will use contrastive pretraining will promote separation between classes.