Securing future decentralised industrial IoT infrastructures: Challenges and free open source solutions

Plaga, Sven; Wiedermann, Norbert; Antón, Simon D. Duque; Tatschner, Stefan; Schotten, Hans D.; Newe, Thomas

doi:10.1016/j.future.2018.11.008

Cited by 35 publications

(15 citation statements)

References 20 publications

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“…The security issues of ICS have been extensively studied. In [12], the authors presented some threats and secure methods for these infrastructures. In [13,14], the authors surveyed some researches on ICS and also presented some challenges that need to be addressed.…”

Section: Related Workmentioning

confidence: 99%

Anomaly Detection for Industrial Control System Based on Autoencoder Neural Network

Wang

Liu

et al. 2020

Wireless Communications and Mobile Computing

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As the Industrial Internet of Things (IIoT) develops rapidly, cloud computing and fog computing become effective measures to solve some problems, e.g., limited computing resources and increased network latency. The Industrial Control Systems (ICS) play a key factor within the development of IIoT, whose security affects the whole IIoT. ICS involves many aspects, like water supply systems and electric utilities, which are closely related to people’s lives. ICS is connected to the Internet and exposed in the cyberspace instead of isolating with the outside recent years. The risk of being attacked increases as a result. In order to protect these assets, intrusion detection systems (IDS) have drawn much attention. As one kind of intrusion detection, anomaly detection provides the ability to detect unknown attacks compared with signature-based techniques, which are another kind of IDS. In this paper, an anomaly detection method with a composite autoencoder model learning the normal pattern is proposed. Unlike the common autoencoder neural network that predicts or reconstructs data separately, our model makes prediction and reconstruction on input data at the same time, which overcomes the shortcoming of using each one alone. With the error obtained by the model, a change ratio is put forward to locate the most suspicious devices that may be under attack. In the last part, we verify the performance of our method by conducting experiments on the SWaT dataset. The results show that the proposed method exhibits improved performance with 88.5% recall and 87.0% F1-score.

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Section: Related Workmentioning

confidence: 99%

Anomaly Detection for Industrial Control System Based on Autoencoder Neural Network

Wang

Liu

et al. 2020

Wireless Communications and Mobile Computing

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“…• Elevation of privilege (EoP) attacks: In EoP attacks, malicious users elevate their privileges or right in order to access resources without permission. Examples of EoP are User-To-Root (U2R) and Remote-to-Local (R2L) [11], [22].…”

Section: B Security Threats In Iotmentioning

confidence: 99%

Security Analysis of Network Anomalies Mitigation Schemes in IoT Networks

2020

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The Internet of Things (IoT) is on the rise and it is giving a new shape to several fields such as smart cities, smart homes, smart health, etc. as it facilitates the connection of physical objects to the internet. However, this advancement comes along with new challenges in terms of security of the devices in the IoT networks. Some of these challenges come as network anomalies. Hence, this has prompted the use of network anomaly mitigation schemes as an integral part of the defense mechanisms of IoT networks in order to protect the devices from malicious users. Thus, several schemes have been proposed to mitigate network anomalies. This paper covers a review of different network anomaly mitigation schemes in IoT networks. The schemes' objectives, operational procedures, and strengths are discussed. A comparison table of the reviewed schemes, as well as a taxonomy based on the detection methodology, is provided. In contrast to other surveys that presented qualitative evaluations, our survey provides both qualitative and quantitative evaluations. The UNSW-NB15 dataset was used to conduct a performance evaluation of some classification algorithms used for network anomaly mitigation schemes in IoT. Finally, challenges and open issues in the development of network anomaly mitigation schemes in IoT are discussed.

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“…There are several examples in the scientific literature of attempts to solve this problem. For instance, some important studies dealt with assembly line sequencing and the optimization of production scheduling in manufacturing processes by requiring set-ups (Kendrick et al 2017;Lou and Van Ryzin 1989;Minguillon and Lanza 2019;Plaga et al 2019;Rolf et al 2020;Sharifnia et al 1991;Fragapane et al 2020;Walker and Bright 2013). Other studies, however, have instead used heuristic methods for the scheduling of flexible flow-shop systems, aiming to find the overall optimum system through better utilization of resources (Paternina-Arboleda et al 2008).…”

Section: Introductionmentioning

confidence: 99%

A novel throughput control algorithm for semi-heterarchical industry 4.0 architecture

et al. 2021

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Modern market scenarios are imposing a radical change in the production concept, driving companies’ attention to customer satisfaction through increased product customization and quick response strategies to maintain competitiveness. At the same time, the growing development of Industry 4.0 technologies made possible the creation of new manufacturing paradigms in which an increased level of autonomy is one of the key concepts to consider. Taking the advantage from the recent development around the semi-heterarchical architecture, this work proposes a first model for the throughput control of a production system managed by such an architecture. A cascade control algorithm is proposed considering work-in-progress (WIP) as the primary control lever for achieving a specific throughput target. It is composed of an optimal control law based on an analytical model of the considered production system, and of a secondary proportional-integral-derivative controller capable of performing an additional control action that addresses the error raised by the theoretical model’s. The proposed throughput control algorithm has been tested in different simulated scenarios, and the results showed that the combination of the control actions made it possible to have continuous adjustment of the WIP of the controlled production system, maintaining it at the minimum value required to achieve the requested throughput with nearly zero errors.

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Securing future decentralised industrial IoT infrastructures: Challenges and free open source solutions

Cited by 35 publications

References 20 publications

Anomaly Detection for Industrial Control System Based on Autoencoder Neural Network

Anomaly Detection for Industrial Control System Based on Autoencoder Neural Network

Security Analysis of Network Anomalies Mitigation Schemes in IoT Networks

A novel throughput control algorithm for semi-heterarchical industry 4.0 architecture

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