Challenges in Implementing Artificial Intelligence on the Raspberry Pi 4, 5 and 5 with AI HAT

Phil Steadman, Paul Jenkins*, Rajkumar Singh Rathore, Chaminda Hewage

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

As technology interacts with all areas of daily life, similarly the risks from cybersecurity attacks have increased proportionally. Furthermore, as the demand for computer-controlled devices and infrastructure has increased, and whilst devices have miniaturized, cybersecurity has not kept pace. Therefore, the importance of malware detection for smaller devices is a necessity. Artificial Intelligence (AI) is improving malware detection, however, training AI models traditionally demands powerful computational resources, far more powerful than the capabilities of lightweight devices such as the Raspberry Pi. Researchers have explored alternative methodologies to adapt AI training to these constraints, given the limited performance while maintaining reliability. This paper examines the design and construction of a lightweight secure network infrastructure tailored to the Raspberry Pi's capabilities. Key considerations include network segmentation, firewall implementation, and device configuration management using automation. Networking setups prioritise wired connectivity for low-latency, high-security applications, and implementing security measures such as browser proxy containers and VPN forwarding to mitigate potential threats, particularly in environments prone to malware infiltration. Training challenges on small-board computers reveal the limitations of Raspberry Pi's computational power for AI training. Comparative data identifies the delta between Raspberry Pi’s and conventional computing devices, concluding the need for more powerful platforms for efficient AI training.

Original languageEnglish
Title of host publicationContributions Presented at The International Conference on Computing, Communication, Cybersecurity and AI - The C3AI 2024
EditorsNitin Naik, Paul Grace, Paul Jenkins, Shaligram Prajapat
PublisherSpringer Science and Business Media Deutschland GmbH
Pages147-157
Number of pages11
ISBN (Print)9783031744426
DOIs
Publication statusPublished - 20 Dec 2024
EventInternational Conference on Computing, Communication, Cybersecurity and AI, C3AI 2024 - London, United Kingdom
Duration: 3 Jul 20244 Jul 2024

Publication series

NameLecture Notes in Networks and Systems
Volume884 LNNS
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

ConferenceInternational Conference on Computing, Communication, Cybersecurity and AI, C3AI 2024
Country/TerritoryUnited Kingdom
CityLondon
Period3/07/244/07/24

Keywords

  • Artificial Intelligence
  • Convolutional neural networks. Image processing
  • Malware
  • Raspberry Pi

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