Evaluating Zero-Trust Configurations for Machine Learning Services in Edge Systems
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Journal Title
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School of Electrical Engineering |
Master's thesis
Authors
Date
2024-12-23
Department
Major/Subject
Cloud and Network Infrastructures
Mcode
Degree programme
Master's Programme in ICT Innovation
Language
en
Pages
50
Series
Abstract
The growing use of Internet of Things systems and Edge Computing for real-time data processing and Machine Learning services has created new security challenges in distributed environments. Because traditional security models rely on perimeter-based security measures and trust presumptions, they often fail to address these issues. Zero Trust emerges as a promising cybersecurity paradigm that works on the principle of "never trust, always verify" to secure the network infrastructure. This thesis investigates the integration of Zero Trust principles in an IoT-Edge system supporting Machine Learning service. Examining Zero Trust Architecture's feasibility in the said system and weighing its performance tradeoffs are the primary goals of the thesis. The study follows a systematic approach that involves creating a Zero Trust proof-of-concept and conducting benchmarks with different Zero Trust configurations to assess their impact on system performance. The results show that integrating Zero Trust enhances the system’s security while keeping performance costs acceptable. These insights extend the understanding of security-performance trade-offs when implementing Zero Trust in edge systems, and offer potential directions for future research in more complex and dynamic environments.Description
Supervisor
Manner, JukkaThesis advisor
Fodor, ViktoriaNaseer, Muhammad Zeshan
Keywords
zero trust, IoT, edge computing, security, networking, performance evaluation