Zero-Trust Principles for Legacy Components-Reference-Cited by-同舟云学术

Zero-Trust Principles for Legacy Components

Published:2021-09-03 Issue:2 Volume:121 Page:1169-1186
ISSN:0929-6212
Container-title:Wireless Personal Communications
language:en
Short-container-title:Wireless Pers Commun

Author:

Køien Geir M.^ORCID

Abstract

AbstractIn this paper we briefly outline as set of rules for integration of legacy devices into a modern industrial control system. These rules are fairly simple, and are mostly derived from “Zero Trust” principles. These rules aim to be pragmatic, and cost-effectiveness trumps completeness.

Funder

University Of South-Eastern Norway

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering,Computer Science Applications

Link

https://link.springer.com/content/pdf/10.1007/s11277-021-09055-1.pdf

Reference24 articles.

1. Alexander, Otis, Belisle, Misha, & Steele, Jacob. (2020). MITRE ATT&CK® for industrial control systems: Design and philosophy (p. 03). MITRE: White paper.

2. Scott Rose, Oliver Borchert, Stu Mitchell, and Sean Connelly. Zero Trust Architecture. NIST Special Publication 800-207, NIST, 08 (2020).

3. ENISA. (2020). Enisa threat landscape: The year in review. ENISA: ETL.

4. DRAGOS. ICS Cybersecurity; Year in review (2020). White paper, DRAGOS, (2020).

5. Shostack A (2014) Threat modeling: Designing for security. Wiley

Cited by 9 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Verify and trust: A multidimensional survey of zero-trust security in the age of IoT;Internet of Things;2024-10

2. Save the Bruised Striver: A Reliable Live Patching Framework for Protecting Real-World PLCs;Proceedings of the Nineteenth European Conference on Computer Systems;2024-04-22

3. Automation and Orchestration of Zero Trust Architecture: Potential Solutions and Challenges;Machine Intelligence Research;2024-01-25

4. Supply Chain Risk Management Through Zero-Trust Architecture;Intelligent Systems for Smart Cities;2024

5. Ensemble-Based Deep Learning Models for Enhancing IoT Intrusion Detection;Applied Sciences;2023-11-02