Exploring Edge TPU for Network Intrusion Detection in IoT

03/30/2021
by   Seyedehfaezeh Hosseininoorbin, et al.
0

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.

READ FULL TEXT

Authors

page 1

page 2

page 3

page 4

10/17/2021

Exploring Deep Neural Networks on Edge TPU

This paper explores the performance of Google's Edge TPU on feed forward...
03/30/2021

E-GraphSAGE: A Graph Neural Network based Intrusion Detection System

This paper presents a new network intrusion detection system (NIDS) base...
05/13/2019

Analyzing Adversarial Attacks Against Deep Learning for Intrusion Detection in IoT Networks

Adversarial attacks have been widely studied in the field of computer vi...
01/29/2020

Compact recurrent neural networks for acoustic event detection on low-energy low-complexity platforms

Outdoor acoustic events detection is an exciting research field but chal...
02/27/2021

Characterization of Neural Networks Automatically Mapped on Automotive-grade Microcontrollers

Nowadays, Neural Networks represent a major expectation for the realizat...
02/01/2019

Efficient Hybrid Network Architectures for Extremely Quantized Neural Networks Enabling Intelligence at the Edge

The recent advent of `Internet of Things' (IOT) has increased the demand...
04/11/2022

Dependable Intrusion Detection System for IoT: A Deep Transfer Learning-based Approach

Security concerns for IoT applications have been alarming because of the...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.