  Edge nodes are crucial for detection against multitudes of cyber attacks on
Internet-of-Things endpoints and is set to become part of a multi-billion
industry. The resource constraints in this novel network infrastructure tier
constricts the deployment of existing Network Intrusion Detection System with
Deep Learning models (DLM). We address this issue by developing a novel light,
fast and accurate 'Edge-Detect' model, which detects Distributed Denial of
Service attack on edge nodes using DLM techniques. Our model can work within
resource restrictions i.e. low power, memory and processing capabilities, to
produce accurate results at a meaningful pace. It is built by creating layers
of Long Short-Term Memory or Gated Recurrent Unit based cells, which are known
for their excellent representation of sequential data. We designed a practical
data science pipeline with Recurring Neural Network to learn from the network
packet behavior in order to identify whether it is normal or attack-oriented.
The model evaluation is from deployment on actual edge node represented by
Raspberry Pi using current cybersecurity dataset (UNSW2015). Our results
demonstrate that in comparison to conventional DLM techniques, our model
maintains a high testing accuracy of 99% even with lower resource utilization
in terms of cpu and memory. In addition, it is nearly 3 times smaller in size
than the state-of-art model and yet requires a much lower testing time.
