Exploring edge TPU for network intrusion detection in IoT

Abstract

This paper explores Google's Edge TPU for implementing a practical network intrusion detection system (NIDS) at the edge of IoT, based on a deep learning approach. While a significant number of related works explore machine learning-based NIDS for the IoT edge, they generally lack considering the issue of the required computational and energy resources. The focus of this paper is the exploration of deep learning-based NIDS at the edge of IoT, and in particular, the computational and energy efficiency. In particular, the paper studies Google's Edge TPU as a hardware platform and considers the following three key metrics: computation (inference) time, energy efficiency and traffic classification performance. Various scaled model sizes of two major deep neural network architectures are used to investigate these three metrics. The performance of the Edge TPU-based implementation is compared with that of an energy-efficient embedded CPU (quad-core ARM Cortex-A53).