A Survey on Resilience in the IoT

Berger, Christian; Eichhammer, Philipp; Reiser, Hans P.; Domaschka, Jörg; Hauck, Franz J.; Habiger, Gerhard

doi:10.1145/3462513

Cited by 43 publications

(18 citation statements)

References 151 publications

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“…Control theory and cybersecurity are research areas that provide significant contributions to solve security issues in CPS from different perspectives. Similarly to the IoT domain [17], resilience in the CPS domain is a dual problem with a part in the cyber world and the other part in the physical one. As pointed out in [13,202], both such domains are complementary disciplines that working together have the potential to provide more efficient and effective solutions.…”

Section: Discussionmentioning

confidence: 99%

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Cyber-Resilience Evaluation of Cyber-Physical Systems

Segovia

Rubio‐Hernan

Cavalli

et al. 2020

2020 IEEE 19th International Symposium on Network Computing and Applications (NCA)

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Concerns for the resilience of Cyber-Physical Systems (CPS) in critical infrastructure are growing. CPS integrate sensing, computation, control and networking into physical objects and mission-critical services, connecting traditional infrastructure to internet technologies.While this integration increases service efficiency, it has to face the possibility of new threats posed by the new functionalities. This leads to cyber-threats, such as denial-of-service, modification of data, information leakage, spreading of malware, and many others.Cyber-resilience refers to the ability of a CPS to prepare, absorb, recover, and adapt to the adverse effects associated with cyber-threats, e.g., physical degradation of the CPS performance resulting from a cyber-attack. Cyber-resilience aims at ensuring CPS survival, by keeping the core functionalities of the CPS in case of extreme events. The literature on cyber-resilience is rapidly increasing, leading to a broad variety of research works addressing this new topic. In this article, we create a systematization of knowledge about existing scientific efforts of making CPS cyber-resilient. We systematically survey recent literature addressing cyber-resilience with a focus on techniques that may be used on CPS. We first provide preliminaries and background on CPS and threats, and subsequently survey state-of-the-art approaches that have been proposed by recent research work applicable to CPS. In particular, we aim at differentiating research work from traditional risk management approaches, based on the general acceptance that it is unfeasible to prevent and mitigate all possible risks threatening a CPS. We also discuss questions and research challenges, with a focus on the practical aspects of cyber-resilience, such as the use of metrics and evaluation methods, as well as testing and validation environments.

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Section: Discussionmentioning

confidence: 99%

“…For this reason, resilience 1 is especially relevant [17]. Developing CPS that can safely survive an attack is a current challenge [18].…”

mentioning

confidence: 99%

Cyber-Resilience Evaluation of Cyber-Physical Systems

Segovia

Rubio‐Hernan

Cavalli

et al. 2020

2020 IEEE 19th International Symposium on Network Computing and Applications (NCA)

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“…This is particularly important given the complex and dynamic nature of these systems [ 54 , 55 ]. The concept of resilience is crucial in the design and operation of IoT systems; however, the practical implementation of resilience mechanisms in IoT systems can be challenging, as they impose constraints on developers [ 56 ]. Despite these challenges, the need for resilient IoT systems is clear, given their increasing integration into critical infrastructures.…”

Section: Iot-dt Frameworkmentioning

confidence: 99%

IoT-Based Framework for Digital Twins in the Industry 5.0 Era

Awouda,

Traini,

Bruno

et al. 2024

Sensors

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Digital twins are considered the next step in IoT-based cyber–physical systems; they allow for the real-time monitoring of assets, and they provide a comprehensive understanding of a system behavior, allowing for data-driven insights and informed choices. However, no comprehensive framework exists for the development of IoT-based digital twins. Moreover, the existing frameworks do not consider the aspects introduced by the Industry 5.0 paradigm, such as sustainability, human-centricity, and resilience. This paper proposes a framework based on the one defined as the outcome of a project funded by the European Union between 2010 and 2013 called the IoT Architectural Reference Model (IoT-A or IoT-ARM), with the aim of the development and implementation of a standard IoT framework that includes digital twins. This framework establishes and implements a standardized collection of architectural instruments for modeling IoT systems in the 5.0 era, serving as a benchmark for the design and implementation of an IoT architecture focused on digital twins and enabling the sustainability, resilience, and human-centricity of the information system. Furthermore, a proof of concept of a monitoring digital twin for a vertical farming system has been developed to test the validity of the framework, and a discussion of applications in the manufacturing and service sectors is presented.

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“…The IoT can be classified into either three-, four-, five-, or seven-layer architectures [ 1 ], while generally, the four-layer architecture is considered the essential component of the IoT [ 2 ]. These four layers are the Perception layer, Network layer, Middleware layer, and Application layer [ 2 , 3 , 4 , 5 ]. The Perception layer contains physical devices such as sensors and actuators that collect data for processing.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning and Deep Learning Techniques for Internet of Things Network Anomaly Detection—Current Research Trends

Rafique,

Abdallah,

Musa

et al. 2024

Sensors

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With its exponential growth, the Internet of Things (IoT) has produced unprecedented levels of connectivity and data. Anomaly detection is a security feature that identifies instances in which system behavior deviates from the expected norm, facilitating the prompt identification and resolution of anomalies. When AI and the IoT are combined, anomaly detection becomes more effective, enhancing the reliability, efficacy, and integrity of IoT systems. AI-based anomaly detection systems are capable of identifying a wide range of threats in IoT environments, including brute force, buffer overflow, injection, replay attacks, DDoS assault, SQL injection, and back-door exploits. Intelligent Intrusion Detection Systems (IDSs) are imperative in IoT devices, which help detect anomalies or intrusions in a network, as the IoT is increasingly employed in several industries but possesses a large attack surface which presents more entry points for attackers. This study reviews the literature on anomaly detection in IoT infrastructure using machine learning and deep learning. This paper discusses the challenges in detecting intrusions and anomalies in IoT systems, highlighting the increasing number of attacks. It reviews recent work on machine learning and deep-learning anomaly detection schemes for IoT networks, summarizing the available literature. From this survey, it is concluded that further development of current systems is needed by using varied datasets, real-time testing, and making the systems scalable.

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A Survey on Resilience in the IoT

Cited by 43 publications

References 151 publications

Cyber-Resilience Evaluation of Cyber-Physical Systems

Cyber-Resilience Evaluation of Cyber-Physical Systems

IoT-Based Framework for Digital Twins in the Industry 5.0 Era

Machine Learning and Deep Learning Techniques for Internet of Things Network Anomaly Detection—Current Research Trends

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