Securing connection between IT and OT: the Fog Intrusion Detection System prospective

Colelli, Riccardo; Panzieri, Stefano; Pascucci, Federica

doi:10.1109/metroi4.2019.8792884

Cited by 6 publications

(4 citation statements)

References 13 publications

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“…Because MQTT is a lightweight small-message protocol, it saves bandwidth and extends the battery life of the device. And since the MQTT client does not require a request update, the Publish / Subscribe architecture is more suited to the edge layer than the Request / Response architecture utilized in other protocols [18]. As a result, different threats such as DoS, brute force attacks, and others are launched against these types of packets, which may be detected by If the packet is normal, it will be directed to the MQTT broker.…”

Section: Securing Connection Between Edge Device and Iiot Machinesmentioning

confidence: 99%

An Efficient Security Model for Industrial Internet of Things (IIoT) System Based on Machine Learning Principles

Qaddoori¹,

Ali²

2023

(AREJ)

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This paper presents a security paradigm for edge devices to defend against various internal and external threats. The first section of the manuscript proposes employing machine learning models to identify MQTT-based (Message Queue Telemetry Transport) attacks using the Intrusion Detection and Prevention System (IDPS) for edge nodes. Because the Machine Learning (ML) model cannot be trained directly on lowperformance platforms (such as edge devices),a new methodology for updating ML models is proposed to provide a tradeoff between the model performance and the computational complexity. The proposed methodology involves training the model on a high-performance computing platform and then installing the trained model as a detection engine on low-performance platforms (such as the edge node of the edge layer)to identify new attacks. Multiple security techniques have been employed in the second half of the manuscript to verify that the exchanged trained model and the exchanged data files are valid and undiscoverable (information authenticity and privacy) and that the source (such as a fog node or edge device) is indeed what it it claimed to be (source authentication and message integrity). Finally, the proposed security paradigm is found to be effective against various internal and external threats and can be applied to a low-cost singleboard computer (SBC).

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Section: Securing Connection Between Edge Device and Iiot Machinesmentioning

confidence: 99%

An Efficient Security Model for Industrial Internet of Things (IIoT) System Based on Machine Learning Principles

Qaddoori¹,

Ali²

2023

(AREJ)

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“…An example of this is the IDS proposed by Kim and Kang [189], which specifically targets the Modbus protocol, a widely used industrial control protocol, and a good example of an existing protocol severely lacking in security mechanisms. Similarly, the MQTT protocol has been covered like this [178]. A second reason can be found in providing protection against Denial of Service (DoS) attacks [115] and improving congestion control in general [187].…”

Section: G Security Monitoringmentioning

confidence: 99%

A Systematic Survey of Industrial Internet of Things Security: Requirements and Fog Computing Opportunities

Tange

Donno

Fafoutis

et al. 2020

IEEE Commun. Surv. Tutorials

277

145

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A key application of the Internet of Things (IoT) paradigm lies within industrial contexts. Indeed, the emerging Industrial Internet of Things (IIoT), commonly referred to as Industry 4.0, promises to revolutionize production and manufacturing through the use of large numbers of networked embedded sensing devices, and the combination of emerging computing technologies, such as Fog/Cloud Computing and Artificial Intelligence. The IIoT is characterized by an increased degree of inter-connectivity, which not only creates opportunities for the industries that adopt it, but also for cyber-criminals. Indeed, IoT security currently represents one of the major obstacles that prevent the widespread adoption of IIoT technology. Unsurprisingly, such concerns led to an exponential growth of published research over the last few years. To get an overview of the field, we deem it important to systematically survey the academic literature so far, and distill from it various security requirements as well as their popularity. This paper consists of two contributions: our primary contribution is a systematic review of the literature over the period 2011-2019 on IIoT Security, focusing in particular on the security requirements of the IIoT. Our secondary contribution is a reflection on how the relatively new paradigm of Fog computing can be leveraged to address these requirements, and thus improve the security of the IIoT.

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“…Aazam et al [50] especially discuss the specific requirements of CPPS, and how fog computing architectures can help to overcome their challenges, with a focus on communication, CPS control, (big) data analysis, and sensing. Colelli et al [51] show how fog resources can be used to improve security in OT and IT networks. Li et al [52] present an approach to make use of machine learning in the fog in order to improve machine maintenance.…”

Section: Fog Computing In Industrial Settingsmentioning

confidence: 99%

The FORA Fog Computing Platform for Industrial IoT

Pop

Zarrin

Barzegaran

et al. 2021

Information Systems

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Securing connection between IT and OT: the Fog Intrusion Detection System prospective

Cited by 6 publications

References 13 publications

An Efficient Security Model for Industrial Internet of Things (IIoT) System Based on Machine Learning Principles

An Efficient Security Model for Industrial Internet of Things (IIoT) System Based on Machine Learning Principles

A Systematic Survey of Industrial Internet of Things Security: Requirements and Fog Computing Opportunities

The FORA Fog Computing Platform for Industrial IoT

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