A Hybrid-Cloud Autoencoder Ensemble Method for BotNets Detection on Edge Devices

We propose a lightweight unsupervised hybrid-cloud ensemble anomaly detection system. We utilize transfer learning to create a model that uses multiple IoT device sources to create a generalized model that requires minimal training to learn new network traffic. These devices feed their output to the cloud enabling more computation while keeping the network traffic secure on the device itself maintaining data privacy. We test this system by creating a simulation testbed to conduct attacks on the IoT Devices to evaluate how well the detection system works. We also compare multiple transfer learned sources to a single source to show how the learning of a target device is impacted.