Development of SCADA Simulator using Omnet++

Ahmad, Zaman; Durad, Muhammad Hanif

doi:10.1109/ibcast.2019.8667158

Cited by 5 publications

(5 citation statements)

References 4 publications

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“…Comparison of simulation tools [203] Vehicle rerouting [204] Industrial control systems [205] NS-3.35 A DES simulator for internet systems primarily used in education and research licensed under the GNU GPLv2 license…”

Section: Software Description Use-casementioning

confidence: 99%

Simulation of Sustainable Manufacturing Solutions: Tools for Enabling Circular Economy

Khan

Abonyi

2022

Sustainability

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At the current worrisome rate of global consumption, the linear economy model of producing goods, using them, and then disposing of them with no thought of the environmental, social, or economic consequences, is unsustainable and points to a deeply flawed manufacturing framework. Circular economy (CE) is presented as an alternative framework to address the management of emissions, scarcity of resources, and economic sustainability such that the resources are kept ‘in the loop’. In the context of manufacturing supply chains (SCs), the 6R’s of rethink, refuse, reduce, reuse, repair, and recycle have been proposed in line with the achievement of targeted net-zero emissions. In order to bring that about, the required changes in the framework for assessing the state of manufacturing SCs with regard to sustainability are indispensable. Verifiable and empirical model-based approaches such as modeling and simulation (M&S) techniques find pronounced use in realizing the ideal of CE. The simulation models find extensive use across various aspects of SCs, including analysis of the impacts, and support for optimal re-design and operation. Using the PRISMA framework to sift through published research, as gathered from SCOPUS, this review is based on 202 research papers spanning from 2015 to the present. This review provides an overview of the simulation tools being put to use in the context of sustainability in the manufacturing SCs, such that various aspects and contours of the collected research articles spanning from 2015 to the present, are highlighted. This article focuses on the three major simulation techniques in the literature, namely, Discrete Event Simulation (DES), Agent-Based Simulation (ABS), and System Dynamics (SD). With regards to their application in manufacturing SCs, each modeling technique has its pros and its cons which are evinced in case of data requirement, model magnification, model resolution, and environment interaction, among others. These limitations are remedied through use of hybrids wherein two or more than two modeling techniques are applied for the desired results. The article also indicates various open-source software solutions that are being employed in research and the industry. This article, in essence, has three objectives. First to present to the prospective researchers, the current state of research, the concerns that have been presented in the field of sustainability modeling, and how they have been resolved. Secondly, it serves as a comprehensive bibliography of peer-reviewed research published from 2015–2022 and, finally, indicating the limitations of the techniques with regards to sustainability assessment. The article also indicates the necessity of a new M&S framework and its prerequisites.

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Section: Software Description Use-casementioning

confidence: 99%

Simulation of Sustainable Manufacturing Solutions: Tools for Enabling Circular Economy

Khan

Abonyi

2022

Sustainability

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“…To generate a dataset, the authors in [44] and [45] used the popular open source network simulator, Network simulator 2 to model a SCADA system. The authors in [6] used Omnet++ simulator to model a SCADA system. In a related work, Queiroz et al [46] used SCADASim for building SCADA system.…”

Section: Datasets Generation Mechanism Overviewmentioning

confidence: 99%

“…[1][2][3][4]. The stringent real-time requirements, growing interconnectivity, standardization of communication protocols and remote accessibility of modern SCADA systems have contributed massively to the exposure of the infrastructures to various vulnerabilities and security challenges such as sabotage, terrorism and intrusions [5][6][7][8]. Historically, when SCADA systems were initially deployed, the goal was to improve CIs' efficiency and effectiveness with little consideration of the potential future security challenges [9,10].…”

Section: Introductionmentioning

confidence: 99%

A Review of Research Works on Supervised Learning Algorithms for SCADA Intrusion Detection and Classification

et al. 2021

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Supervisory Control and Data Acquisition (SCADA) systems play a significant role in providing remote access, monitoring and control of critical infrastructures (CIs) which includes electrical power systems, water distribution systems, nuclear power plants, etc. The growing interconnectivity, standardization of communication protocols and remote accessibility of modern SCADA systems have contributed massively to the exposure of SCADA systems and CIs to various forms of security challenges. Any form of intrusive action on the SCADA modules and communication networks can create devastating consequences on nations due to their strategic importance to CIs’ operations. Therefore, the prompt and efficient detection and classification of SCADA systems intrusions hold great importance for national CIs operational stability. Due to their well-recognized and documented efficiencies, several literature works have proposed numerous supervised learning techniques for SCADA intrusion detection and classification (IDC). This paper presents a critical review of recent studies whereby supervised learning techniques were modelled for SCADA intrusion solutions. The paper aims to contribute to the state-of-the-art, recognize critical open issues and offer ideas for future studies. The intention is to provide a research-based resource for researchers working on industrial control systems security. The analysis and comparison of different supervised learning techniques for SCADA IDC systems were critically reviewed, in terms of the methodologies, datasets and testbeds used, feature engineering and optimization mechanisms and classification procedures. Finally, we briefly summarized some suggestions and recommendations for future research works.

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“…Academic researchers typically employ simulators with an open license, e.g., OMNeT++ [9] (i.e., the free version of OMNEST), ns-2 [10], ns- 3 [11] or JSim [12]. These are generic network simulators, although most of them include models for wireless interfaces that are used in the IoT environment (802.11, 6LowPAN, LoRa, LTE) [13] [14], and for industrial network scenarios [15] [16] [17]. Software designed for other specific environments also exists [18], such as simulators for wireless networks [19], vehicular networks [20] or sensor networks [21].…”

mentioning

confidence: 99%

“…We have added OMNeT++ to the list for comparison because it is also widely used in academia; it has multiple TCP simulation models, some other simulators are derived from it [14], it offers models for IIoT scenarios [15] [16] and, although there is a version for the industrial environment that is available through a license (OMNEST), there is also a free version for academic researchers.…”

mentioning

confidence: 99%

Network Simulation in a TCP-Enabled Industrial Internet of Things Environment-Reproducibility Issues for Performance Evaluation

Morató

Perez-Gomara

Magaña

et al. 2022

IEEE Trans. Ind. Inf.

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Network simulation is a tool used to analyse and predict the performance of Industrial Internet of Things deployments while dealing with the complexity of real testbeds. Large network deployments with complex protocols such as Transmission Control Protocol are subject to chaos-theory behaviour, i.e. small changes in the implementation of the protocol stack or simulator behaviour may result in large differences in the performance results. We present the results of simulating two different scenarios using three simulators. The first scenario focuses on the Incast phenomenon in a local area network where sensor data are collected. The second scenario focuses on a congested link traversed by the collected measurements. The performance metrics obtained from the simulators are compared among them and with ground-truth obtained from real network experiments. The results demonstrate how subtle implementation differences in network simulators impact performance results, and how network engineers must consider these differences.Index Terms-Computer simulation, Internet of Things, Reproducibility I. INTRODUCTIONT HE Industrial Internet of Things (IIoT) is adding large numbers of devices to communication networks. In this era, new devices are connected and old ones are removed, equipment from different vendors is added, old applications stop being used and new ones are developed, controller servers are moved from one switch to another or even to a remote site, new links with increased capacity are added, and new routing protocols are deployed, perhaps without removing old ones, all of this at a really fast pace. Any of these changes can yield consequences in network performance that are very difficult to predict. In complex systems such as modern networks, because of non-linearities and synchronisations, small changes can yield large-scale consequences. This phenomenon has been referred to as the amplification principle [1].At present, it is unfeasible to predict network behaviour and performance using analytical tools. This is because a large number of elements must be considered in the model, including non-deterministic behaviours (e.g., user actions).

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Development of SCADA Simulator using Omnet++

Cited by 5 publications

References 4 publications

Simulation of Sustainable Manufacturing Solutions: Tools for Enabling Circular Economy

Simulation of Sustainable Manufacturing Solutions: Tools for Enabling Circular Economy

A Review of Research Works on Supervised Learning Algorithms for SCADA Intrusion Detection and Classification

Network Simulation in a TCP-Enabled Industrial Internet of Things Environment-Reproducibility Issues for Performance Evaluation

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