  Deploying machine learning-based intrusion detection systems (IDSs) on
hardware devices is challenging due to their limited computational resources,
power consumption, and network connectivity. Hence, there is a significant need
for robust, deep learning models specifically designed with such constraints in
mind. In this paper, we present a design methodology that automatically trains
and evolves quantized neural network (NN) models that are a thousand times
smaller than state-of-the-art NNs but can efficiently analyze network data for
intrusion at high accuracy. In this regard, the number of LUTs utilized by this
network when deployed to an FPGA is between 2.3x and 8.5x smaller with
performance comparable to prior work.
