Network Intrusion Detection on the IoT Edge Using Adversarial Autoencoders

Network intrusion detection systems have received a lot of attention in the computer security literature. As the number of IoT devices grows exponentially, intrusion detection on the back-end servers or indeed even the fog will become intractable. Consequently, there is a need to move intrusion detection closer to the IoT edge. Doing so will have a significant impact on the network as well as the compute required on the server-side. In this paper, we show how deep learning can be used to build state-of-the intrusion detection algorithms that can be executed on small routers near the IoT edge. Adversarial autoencoders with the K nearest neighbor algorithm were trained on the NSL-KDD intrusion data set to yield state-of-the-art results. The model had an accuracy of 99.991% and an F1-Score of 0.9990. On a Raspberry PI 3B (RPI) device, using TensorFlow Lite, the model achieved an average per-packet latency of less than 16ms which is sufficient for many IoT sensors on the edge giving a worst-case bandwidth of 3kibts/second.