Cyber Resilience in IoT Networks Revisited: Frameworks, Mechanisms, and Future Directions

The rising number of cyber intrusions targeting IoT networks—widely deployed in critical domains such as smart transportation, healthcare, and urban infrastructure—highlights the limitations of traditional security approaches in countering evolving threats. As cyber attackers become more sophisticated and adaptive, they can outsmart traditional intrusion detection and prevention systems, compromising the integrity and availability of these critical networks. Recognising the inevitability of cyber attacks and their potentially severe impacts, researchers have shifted from purely preventive security measures to a resilience-focused approach that prioritises rapid response and recovery to minimise disruptions. A comprehensive and standardised framework, developed based on the specific and unique requirements of IoT networks, can serve as a theoretical and practical foundation for designing and implementing effective and resilient mechanisms. This survey examines the concept of cyber resilience in IoT networks, with a focus on both framework development and the critique of proposed mechanisms. It introduces a customised cyber resilience framework tailored for IoT networks, based on the Cyber Resilience Framework by the National Institute of Standards and Technology (NIST). Also, it provides a roadmap and structured development process to facilitate the implementation of resilience mechanisms. The survey categorises existing cyber resilience mechanisms for IoT networks based on the techniques and approaches used and evaluates their effectiveness in achieving cyber resilience goals using the customised framework.

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Publication Details

Journal
ACM Computing Surveys
Published
2026-05-30
DOI
https://doi.org/10.1145/3818682
Primary Topic
Network Security and Intrusion Detection
Type
article
Field-Weighted Citation Impact
0.00
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article

Cyber Resilience in IoT Networks Revisited: Frameworks, Mechanisms, and Future Directions

Liqun Chen, Muhammad Rizwan Asghar, Foroozan Darbandeh
ACM Computing Surveys
Network Security and Intrusion Detection
article

Cyber Resilience in IoT Networks Revisited: Frameworks, Mechanisms, and Future Directions

Liqun Chen, Muhammad Rizwan Asghar, Foroozan Darbandeh
article en

Abstract

The rising number of cyber intrusions targeting IoT networks—widely deployed in critical domains such as smart transportation, healthcare, and urban infrastructure—highlights the limitations of traditional security approaches in countering evolving threats. As cyber attackers become more sophisticated and adaptive, they can outsmart traditional intrusion detection and prevention systems, compromising the integrity and availability of these critical networks. Recognising the inevitability of cyber attacks and their potentially severe impacts, researchers have shifted from purely preventive security measures to a resilience-focused approach that prioritises rapid response and recovery to minimise disruptions. A comprehensive and standardised framework, developed based on the specific and unique requirements of IoT networks, can serve as a theoretical and practical foundation for designing and implementing effective and resilient mechanisms. This survey examines the concept of cyber resilience in IoT networks, with a focus on both framework development and the critique of proposed mechanisms. It introduces a customised cyber resilience framework tailored for IoT networks, based on the Cyber Resilience Framework by the National Institute of Standards and Technology (NIST). Also, it provides a roadmap and structured development process to facilitate the implementation of resilience mechanisms. The survey categorises existing cyber resilience mechanisms for IoT networks based on the techniques and approaches used and evaluates their effectiveness in achieving cyber resilience goals using the customised framework.

ACM Computing Surveys
University of Auckland (NZ), University of Surrey (GB)
Openalex Percentile: Top 3%
Network Security and Intrusion Detection
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