Decision model of intrusion response based on markov game in fog computing environment

Ma, Xin; Li, Yun; Gao, Yan

doi:10.1007/s11276-023-03382-w

Cited by 2 publications

(1 citation statement)

References 24 publications

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“…• With augmented traffic in the network, there is a high suspicion of distortions, leading to a loss of coherence in the data that could potentially cause false-positive alarms; • Regrettably, the quantity of false-positive alarms surpasses the potential attacks by intruders-therefore, a limitation is to accurately differentiate these genuine attacks from the irrelevant ones; • A challenge in classifying alarms arises due to the diversity of firmware versions-it is imperative to keep the devices' versions in the network as current as possible and to utilize devices from vendors with minimal diversification [30][31][32][33][34].…”

Section: Limitations Of Own Researchmentioning

confidence: 99%

Autonomous Threat Response at the Edge Processing Level in the Industrial Internet of Things

Czeczot,

Rojek,

Mikołajewski

2024

Electronics

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Industrial Internet of Things (IIoT) technology, as a subset of the Internet of Things (IoT) in the concept of Industry 4.0 and, in the future, 5.0, will face the challenge of streamlining the way huge amounts of data are processed by the modules that collect the data and those that analyse the data. Given the key features of these analytics, such as reducing the cost of building massive data centres and finding the most efficient way to process data flowing from hundreds of nodes simultaneously, intermediary devices are increasingly being used in this process. Fog and edge devices are hardware devices designed to pre-analyse terabytes of data in a stream and decide in realtime which data to send for final analysis, without having to send the data to a central processing unit in huge local data centres or to an expensive cloud. As the number of nodes sending data for analysis via collection and processing devices increases, so does the risk of data streams being intercepted. There is also an increased risk of attacks on this sensitive infrastructure. Maintaining the integrity of this infrastructure is important, and the ability to analyse all data is a resource that must be protected. The aim of this paper is to address the problem of autonomous threat detection and response at the interface of sensors, edge devices, cloud devices with historical data, and finally during the data collection process in data centres. Ultimately, we would like to present a machine learning algorithm with reinforcements adapted to detect threats and immediately isolate infected nests.

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Section: Limitations Of Own Researchmentioning

confidence: 99%

Autonomous Threat Response at the Edge Processing Level in the Industrial Internet of Things

Czeczot,

Rojek,

Mikołajewski

2024

Electronics

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Analytical Approaches to QoS Analysis and Performance Modelling in Fog Computing

Kirsal

2023

Multi-Disciplinary Applications of Fog Computing

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This book chapter focuses on the analysis of quality of service (QoS) and performance modelling in the context of fog computing. The chapter proposes analytical frameworks for QoS analysis and performance modelling of fog computing systems. The chapter starts with an introduction to fog computing and the importance of QoS analysis in such systems. The next section presents a literature review of related work and different approaches to QoS analysis in fog computing. The proposed analytical frameworks are then described in detail, including their different components and assumptions. Case studies are also presented to demonstrate the application of the analytical frameworks. The case studies include a scenario of a fog computing system with a specific architecture and different performance metrics and models used for the analysis. The results and analysis of the case studies are then presented. Finally, the chapter concludes with a discussion of the key findings and contributions of the analytical frameworks.

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Decision model of intrusion response based on markov game in fog computing environment

Cited by 2 publications

References 24 publications

Autonomous Threat Response at the Edge Processing Level in the Industrial Internet of Things

Autonomous Threat Response at the Edge Processing Level in the Industrial Internet of Things

Analytical Approaches to QoS Analysis and Performance Modelling in Fog Computing

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