A Tiny Transformer-Based Anomaly Detection Framework for IoT Solutions

Abstract

The widespread proliferation of Internet of Things (IoT) devices has pushed for the development of novel transformer-based Anomaly Detection (AD) tools for an accurate monitoring of functionalities in industrial systems. Despite their outstanding performances, transformer models often rely on large Neural Networks (NNs) that are difficult to be executed by IoT devices due to their energy/computing constraints. This paper focuses on introducing tiny transformer-based AD tools to make them viable solutions for on-device AD. Starting from the state-of-the-art Anomaly Transformer (AT) model, which has been shown to provide accurate AD functionalities but it is characterized by high computational and memory demand, we propose a tiny AD framework that finds an optimized configuration of the AT model and uses it for devising a compressed version compatible with resource-constrained IoT systems. A knowledge distillation tool is developed to obtain a highly compressed AT model without degrading the AD performance. The proposed framework is firstly analyzed on four widely-adopted AD datasets and then assessed using data extracted from a real-world monitoring facility. The results show that the tiny AD tool provides a compressed AT model with a staggering 99.93% reduction in the number of trainable parameters compared to the original implementation (from 4.8 million to 3300 or 1400 according to the input dataset), without significantly compromising the accuracy in AD. Moreover, the compressed model substantially outperforms a popular Recurrent Neural Network (RNN)-based AD tool having a similar number of trainable weights as well as a conventional One-Class Support Vector Machine (OCSVM) algorithm.