research-article

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"If security is required": engineering and security practices for machine learning-based IoT devices

Authors:

Nikhil Krishna Gopalakrishna,

Dharun Anandayuvaraj,

Forrest Lee Bland,

Sazzadur Rahaman,

James C. DavisAuthors Info & Claims

SERP4IoT '22: Proceedings of the 4th International Workshop on Software Engineering Research and Practice for the IoT

Pages 1 - 8

https://doi.org/10.1145/3528227.3528565

Published: 03 February 2023 Publication History

Abstract

The latest generation of IoT systems incorporate machine learning (ML) technologies on edge devices. This introduces new engineering challenges to bring ML onto resource-constrained hardware, and complications for ensuring system security and privacy. Existing research prescribes iterative processes for machine learning enabled IoT products to ease development and increase product success. However, these processes mostly focus on existing practices used in other generic software development areas and are not specialized for the purpose of machine learning or IoT devices.

This research seeks to characterize engineering processes and security practices for ML-enabled IoT systems through the lens of the engineering lifecycle. We collected data from practitioners through a survey (N=25) and interviews (N=4). We found that security processes and engineering methods vary by company. Respondents emphasized the engineering cost of security analysis and threat modeling, and trade-offs with business needs. Engineers reduce their security investment if it is not an explicit requirement. The threats of IP theft and reverse engineering were a consistent concern among practitioners when deploying ML for IoT devices. Based on our findings, we recommend further research into understanding engineering cost, compliance, and security trade-offs.

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"If security is required": engineering and security practices for machine learning-based IoT devices

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cover image ACM Conferences

SERP4IoT '22: Proceedings of the 4th International Workshop on Software Engineering Research and Practice for the IoT

May 2022

48 pages

ISBN:9781450393324

DOI:10.1145/3528227

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Published: 03 February 2023

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Cited By

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https://dl.acm.org/doi/10.1145/3674805.3686665
Schorlemmer TKalu KChigges LKo KIshgair EBagchi STorres-Arias SDavis J(2024)Signing in Four Public Software Package Registries: Quantity, Quality, and Influencing Factors2024 IEEE Symposium on Security and Privacy (SP)10.1109/SP54263.2024.00215(1160-1178)Online publication date: 19-May-2024
https://doi.org/10.1109/SP54263.2024.00215
Jiang WBanna VVivek NGoel ASynovic NThiruvathukal GDavis J(2024)Challenges and practices of deep learning model reengineering: A case study on computer visionEmpirical Software Engineering10.1007/s10664-024-10521-029:6Online publication date: 20-Aug-2024
https://doi.org/10.1007/s10664-024-10521-0
Romare P(2024)User-Driven Privacy Factors in Trigger-Action Apps: A Comparative Analysis with General IoTPrivacy and Identity Management. Sharing in a Digital World10.1007/978-3-031-57978-3_16(244-264)Online publication date: 23-Apr-2024
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