# Exploring Edge TPU for Network Intrusion Detection in IoT

arXiv: https://arxiv.org/abs/2103.16295 · HTML: https://mlsanity.com/paper/2103.16295/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 41 references that are in the map.
- First posted on arXiv 30 Mar 2021.

## Builds on (most cited first, 5 of 5)

- [In-Datacenter Performance Analysis of a Tensor Processing Unit](https://mlsanity.com/paper/1704.04760/) (2017)
- [Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection](https://mlsanity.com/paper/1802.09089/) (2018)
- [Towards the Development of Realistic Botnet Dataset in the Internet of Things for Network Forensic Analytics: Bot-IoT Dataset](https://mlsanity.com/paper/1811.00701/) (2018)
- [IoT Sentinel: Automated Device-Type Identification for Security Enforcement in IoT](https://mlsanity.com/paper/1611.04880/) (2016)
- [Deep Learning-based Cattle Activity Classification Using Joint Time-frequency Data Representation](https://mlsanity.com/paper/2011.03381/) (2020)

## Built on by (most cited first, 5 of 5)

- [On Generalisability of Machine Learning-based Network Intrusion Detection Systems](https://mlsanity.com/paper/2205.04112/) (2022)
- [Network Intrusion Detection System in a Light Bulb](https://mlsanity.com/paper/2210.03254/) (2022)
- [Temporal Analysis of NetFlow Datasets for Network Intrusion Detection Systems](https://mlsanity.com/paper/2503.04404/) (2025)
- [Exploring Deep Neural Networks on Edge TPU](https://mlsanity.com/paper/2110.08826/) (2021)
- [QPART: Adaptive Model Quantization and Dynamic Workload Balancing for Accuracy-aware Edge Inference](https://mlsanity.com/paper/2506.23934/) (2025)

## Abstract (arXiv)

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 there are a significant number of related works that explore machine learning based NIDS for the IoT edge, they generally do not consider 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 the 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 (ARM Cortex A53). Our experimental evaluation shows some unexpected results, such as the fact that the CPU significantly outperforms the Edge TPU for small model sizes.