  Anomaly detection is widely used in a broad range of domains from
cybersecurity to manufacturing, finance, and so on. Deep learning based anomaly
detection has recently drawn much attention because of its superior capability
of recognizing complex data patterns and identifying outliers accurately.
However, deep learning models are typically iteratively optimized in a central
server with input data gathered from edge devices, and such data transfer
between edge devices and the central server impose substantial overhead on the
network and incur additional latency and energy consumption. To overcome this
problem, we propose a fully-automated, lightweight, statistical learning based
anomaly detection framework called LightESD. It is an on-device learning method
without the need for data transfer between edge and server, and is extremely
lightweight that most low-end edge devices can easily afford with negligible
delay, CPU/memory utilization, and power consumption. Yet, it achieves highly
competitive detection accuracy. Another salient feature is that it can
auto-adapt to probably any dataset without manually setting or configuring
model parameters or hyperparameters, which is a drawback of most existing
methods. We focus on time series data due to its pervasiveness in edge
applications such as IoT. Our evaluation demonstrates that LightESD outperforms
other SOTA methods on detection accuracy, efficiency, and resource consumption.
Additionally, its fully automated feature gives it another competitive
advantage in terms of practical usability and generalizability.
