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Drinking fresh water, turning the lights on, travelling by tram, calling our family, or getting a medical treatment are usual activities, but the underlying SCADA (Supervisory Control and Data Acquisition) systems like CIS (Critical Infrastructure Systems), ICS (Industrial Control Systems) or DCS (Distributed Control Systems) were always the target...
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... This will improve the flexibility of these personnel and hence it will result in improving open thinking and productivity. A platform that can acquire data from DCS or PLC [6] in real time, with capability to analyze and visualize on static as well as mobile devices with alerts for manual interventions as needed, can support industry to meet this requirement. As the sensors, wireless connectivity, computing and visualizing capabilities are in the developed phase, an Internet of Things (IoT) [7] based platform will be the right choice for meeting this requirement. ...
... Component of SCADA system[9]. ...
The aim of this study is to explore the potential and economic benefits of utilising Supervisory Control and Data Acquisition (SCADA) data to improve wind turbine operation and maintenance activities. The review identifies a gap in the current understanding of how to effectively use SCADA data in wind turbine applications. It emphasises the need for pre‐processing SCADA data to ensure data integrity by addressing outliers and employing interpolation techniques. Additionally, it highlights the challenges associated with early fault detection methods using SCADA data, including the development of physical models, data‐driven machine learning models, and statistical regression models. The review also recognises the limitations caused by the lack of public data from wind turbine developers and the imbalance between normal operation data samples and abnormal data samples, negatively impacting model accuracy. The key findings of the review demonstrate that SCADA data‐driven techniques can lead to significant improvements in wind turbine operations and maintenance. The application of data‐driven technologies based on SCADA data has proven effective in reducing operation and maintenance costs and enhancing wind power generation. Moreover, the development of robust decision support systems using SCADA data minimises the need for frequent maintenance interventions in offshore wind farms. To bridge the gap and further enhance wind turbine applications using SCADA data, several recommendations are provided. These include encouraging greater openness in sharing SCADA data to improve the robustness and accuracy of AI models, adopting transfer learning techniques to overcome the scarcity of quality datasets, establishing unified standards and taxonomies, and providing specialised resources such as software applications with interactive graphical user interfaces for easier storage, annotation, and analysis of SCADA data.
The authors’ review paper identifies a gap in the current understanding of how to effectively utilise SCADA data in wind turbine applications. It emphasises the importance of pre‐processing SCADA data to ensure data integrity by addressing outliers and employing interpolation techniques. Furthermore, the authors highlight the challenges associated with early fault detection methods using SCADA data, including the development of physical models, data‐driven machine learning models, and statistical regression models.
... includes tracking the status of alarms and data processing. The integration of wireless and direct wired systems is supported by this technique [41]. ...
All life on earth depends on water as a resource. The availability of water is dwindling over time. Water is a renewable resource that, if not managed, will eventually run out of resources. The artificial intelligence can play a very important role in water conservation, especially in developing countries such as India. The employment of artificial intelligence in modelling approaches produces answers for linear, non-linear, and other systems that are close to the real data, as shown by a number of studies in the literature. For the following water variables such as rainfall-runoff, evaporation and evapotranspiration, water quality components, streamflow, sediment, and changes in dams or lakes levels, we looked at the state-of-the-art and advancements in artificial intelligence modelling. This article investigates the function of artificial intelligence in water conservation in order to achieve the goal of serving as a reference for the general public. It accomplishes this by fully introducing this notion, its import, and relevant application scenarios. The study has also offered a number of interesting research topics and models for the water variable variables.KeywordsEvapotranspirationArtificial intelligenceWater qualityRunoffSediment
... Thus, various ways to ensure the security of CPSs have been enacted. For example, the National Institute of Standards and Technology (NIST) provides regulation for the security of cyber systems [28,29]. Additionally, the International Society of Automation (ISA) provides the regulation for cyber-based industrial process control systems [30]. ...
With the rapid proliferation of cyber-physical systems (CPSs) in various sectors, including critical infrastructure, transportation, healthcare, and the energy industry, there is a pressing need for robust cybersecurity mechanisms to protect these systems from cyberattacks. A cyber-physical system is a combination of physical and cyber components, and a security breach in either component can lead to catastrophic consequences. Cyberattack detection and mitigation methods in CPSs involve the use of various techniques such as intrusion detection systems (IDSs), firewalls, access control mechanisms, and encryption. Overall, effective cyberattack detection and mitigation methods in CPSs require a comprehensive security strategy that considers the unique characteristics of a CPS, such as the interconnectedness of physical and cyber components, the need for real-time response, and the potential consequences of a security breach. By implementing these methods, CPSs can be better protected against cyberattacks, thus ensuring the safety and reliability of critical infrastructure and other vital systems. This paper reviews the various kinds of cyber-attacks that have been launched or implemented in CPSs. It reports on the state-of-the-art detection and mitigation methods that have been used or proposed to secure the safe operation of various CPSs. A summary of the requirements that CPSs need to satisfy their operation is highlighted, and an analysis of the benefits and drawbacks of model-based and data-driven techniques is carried out. The roles of machine learning in cyber assault are reviewed. In order to direct future study and motivate additional investigation of this increasingly important subject, some challenges that have been unaddressed, such as the prerequisites for CPSs, an in-depth analysis of CPS characteristics and requirements, and the creation of a holistic review of the different kinds of attacks on different CPSs, together with detection and mitigation algorithms, are discussed in this review.
... These technologies include SCADA and PLC. SCADA is a system of software and hardware elements that is generally used to control dispersed assets and industrial processes locally or in remote locations using centralized data acquisition and supervisory control [5,7,[32][33][34]. It intends to monitor, collect, and process real-time data remotely while directly connecting and interacting with devices such as sensors through human-machine interface software [5,7,32,33]. ...
... SCADA is a system of software and hardware elements that is generally used to control dispersed assets and industrial processes locally or in remote locations using centralized data acquisition and supervisory control [5,7,[32][33][34]. It intends to monitor, collect, and process real-time data remotely while directly connecting and interacting with devices such as sensors through human-machine interface software [5,7,32,33]. As such, SCADA can be used for multiple warehouses scattered over wider geographic locations. ...
... PLC is a microprocessor-based, solid-state industrial computer control system that performs discrete or sequential logic to create a computer network in remotely located SCADA is a system of software and hardware elements that is generally used to control dispersed assets and industrial processes locally or in remote locations using centralized data acquisition and supervisory control [5,7,[32][33][34]. It intends to monitor, collect, and process real-time data remotely while directly connecting and interacting with devices such as sensors through human-machine interface software [5,7,32,33]. ...
Background: The unprecedented supply chain disruptions caused by the prolonged COVID-19 pandemic forced many firms to change their way of doing business dramatically. These changes include quickly responding to the growing demand for online orders and the corresponding direct shipments to customer locations. These changes have been further accelerated by rapid technological innovations resulting from the fourth industrial revolution (Industry 4.0). One of the most notable technological transformations that we have witnessed is the growing popularity of smart warehousing concepts. Although smart warehousing may represent a wave of the warehousing future, the published literature rarely documents its underlying principles, specific application targets, and potential impacts on supply chain performance. This research aims to identify key drivers of the digital warehousing revolution and describe important value propositions for warehousing automation. Methods: To help companies develop smart warehouses successfully as an integral part of a supply chain link, I conceptualize an ideal smart warehousing system, design its basic architecture, propose specific milestones for monitoring the progress of smart warehouse development, and then, identify critical success factors for its full utilization in today’s volatile warehousing environment. This paper employed qualitative content analysis to conceptualize smart warehousing development and establish a smart warehousing framework. Results: A smart warehouse will bring many managerial benefits, including warehousing cost efficiency, labor productivity, and agility in the era of the knowledge economy. Conclusions: This paper will enable companies to accelerate digital transformation and improve their competitiveness amid the post-pandemic industrial revolution.
... SCADA is a category of software application program for process control, which gathers data in real time from remote locations to control equipment and conditions. SCADA is used in power plants as well as in oil and gas refining, telecommunications, transportation, and water and waste control (Boyer, 2009;Stouffer et al., 2006). Thus, it is important to evaluate risks on a regular basis and ensure that the most valuable organizational assets are protected with appropriate controls. ...
... SCADA-type systems are made up of two distinct components, a hardware component necessary to achieve connectivity between the monitored equipment and the monitoring center and a software component that includes the settings of the equipment used, the automation and control programs implemented in the monitored equipment as well as the necessary software for storage and remote control contained in the dispatch part of the project [1,2]. To be able to acquire and control remote equipment, distributed technological processes use dedicated communication equipment of the RTU type that integrates the equipment necessary to communicate with the SCADA center and one or more secondary communication paths with the role of controlling and acquiring data from local equipment monitored and controlled remotely via RTU as seen in Figure 1. ...
... To be able to acquire and control remote equipment, distributed technological processes use dedicated communication equipment of the RTU type that integrates the equipment necessary to communicate with the SCADA center and one or more secondary communication paths with the role of controlling and acquiring data from local equipment monitored and controlled remotely via RTU as seen in Figure 1. Depending on the complexity and importance of the monitored process, two distinct command structures can be created [1,2]. In cases where the amount of information exchanged with the control center is small, integrated RTU type equipment is used that has a compact structure and integrates a small number of digital and analog inputs and outputs. ...
... In cases where the amount of information exchanged with the control center is small, integrated RTU type equipment is used that has a compact structure and integrates a small number of digital and analog inputs and outputs. If the technical solution is more complex, or it is necessary to achieve local control over the process, then the hardware structure requires the use of a PLC, an HMI and a standard communication equipment for the interface with the SCADA center [2,3]. Using a PLC imposes configuring and writing a program code capable of acquiring and storing data as well as scaling them so that they can be viewed in an accessible form on the display of a local HMI [3][4][5]. ...
In the present paper we describe a method for the design and construction of a remote measurement and control point usable in a water supply project. The structure of the project is designed in such a way as to allow incorporation into a larger SCADA-type project. The proposed solution is a generic one that can be realized with the help of a small PLC and a GSM communication equipment or radio modem. The work includes both the electrical design of the assembly as well as the software configuration used together with the structure of the PLC program.
... A typical ICS consists of several components, including sensors, actuators and Programmable Logic Controllers (PLCs), which act together to achieve industrial objectives such as water treatment. These components interact using an array of industrial network protocols on a layered network architecture [18]. A sensor is a device that measures a physical quantity or phenomena and converts it proportionally to an electric voltage or current. ...
... Find index of the minimum score 16: for i = 0 to len(B g ) do end if 22: end if the lowest RF score (lines [12][13][14][15][16][17][18][19][20][21][22][23][24]. Specifically, in line 13, the algorithm verifies that sensor readings of the new alarm do not already exist in the buffer. ...
... The RF score of every instance in the buffer is computed, and the index of the lowest score is found (lines [14][15]. Then, the recency score for every instance in the buffer is decremented (lines [16][17][18]. Finally, in lines 19-20, the newly created instance replaces the instance with the smallest RF score in the buffer and the similarity threshold is updated based on the new instance. ...
Critical Infrastructures (CIs) such as water treatment plants, power grids and telecommunication networks are critical to the daily activities and well-being of our society. Disruption of such CIs would have catastrophic consequences for public safety and the national economy. Hence, these infrastructures have become major targets in the upsurge of cyberattacks. Defending against such attacks often depends on an arsenal of cyber-defence tools, including Machine Learning (ML)-based Anomaly Detection Systems (ADSs). These detection systems use ML models to learn the profile of the normal behaviour of a CI and classify deviations that go well beyond the normality profile as anomalies. However, ML methods are vulnerable to both adversarial and non-adversarial input perturbations. Adversarial perturbations are imperceptible noises added to the input data by an attacker to evade the classification mechanism. Non-adversarial perturbations can be a normal behaviour evolution as a result of changes in usage patterns or other characteristics and noisy data from normally degrading devices, generating a high rate of false positives.
We first study the problem of ML-based ADSs being vulnerable to non-adversarial perturbations, which causes a high rate of false alarms. To address this problem, we propose an ADS called DAICS, based on a wide and deep learning model that is both adaptive to evolving normality and robust to noisy data normally emerging from the system. DAICS adapts the pre-trained model to new normality with a small number of data samples and a few gradient updates based on feedback from the operator on false alarms. The DAICS was evaluated on two datasets collected from real-world Industrial Control System (ICS) testbeds. The results show that the adaptation process is fast and that DAICS has an improved robustness compared to state-of-the-art approaches.
We further investigated the problem of false-positive alarms in the ADSs. To address this problem, an extension of DAICS, called the SiFA framework, is proposed. The SiFA collects a buffer of historical false alarms and suppresses every new alarm that is similar to these false alarms. The proposed framework is evaluated using a dataset collected from a real-world ICS testbed. The evaluation results show that the SiFA can decrease the false alarm rate of DAICS by more than 80%.
We also investigate the problem of ML-based network ADSs that are vulnerable to adversarial perturbations. In the case of network ADSs, attackers may use their knowledge of anomaly detection logic to generate malicious traffic that remains undetected. One way to solve this issue is to adopt adversarial training in which the training set is augmented with adversarially perturbed samples. This thesis presents an adversarial training approach called GADoT that leverages a Generative Adversarial Network (GAN) to generate adversarial samples for training. GADoT is validated in the scenario of an ADS detecting Distributed Denial of Service (DDoS) attacks, which have been witnessing an increase in volume and complexity. For a practical evaluation, the DDoS network traffic was perturbed to generate two datasets while fully preserving the semantics of the attack. The results show that adversaries can exploit their domain expertise to craft adversarial attacks without requiring knowledge of the underlying detection model. We then demonstrate that adversarial training using GADoT renders ML models more robust to adversarial perturbations.
However, the evaluation of adversarial robustness is often susceptible to errors, leading to robustness overestimation. We investigate the problem of robustness overestimation in network ADSs and propose an adversarial attack called UPAS to evaluate the robustness of such ADSs. The UPAS attack perturbs the inter-arrival time between packets by injecting a random time delay before packets from the attacker. The attack is validated by perturbing malicious network traffic in a multi-attack dataset and used to evaluate the robustness of two robust ADSs, which are based on a denoising autoencoder and an adversarially trained ML model. The results demonstrate that the robustness of both ADSs is overestimated and that a standardised evaluation of robustness is needed.
... SCADA becomes popular in the 1960's for a variety of reasons. It enables the achievement of monitoring and controlling the plant operation remotely using communication technology [3].Many automation companies are using the SCADA to provide access to real-time data display, alarming, treating, and reporting from remote sites. It gives the flexibility to choose equipment and systems based on performance rather than compatibility with installed base [4]. ...
... TOS can be linked and integrated with both ERP and Electronic Data Interchange (EDI). SCADA is a system of software and hardware elements that are generally used to control dispersed assets and industrial processes locally, or at remote locations, using centralized data acquisition and supervisory control (Stouffer and Falco 2006;Boyer 2009;Inductive Automation 2018). It intends to monitor, gather, and process real-time data, while directly interacting with devices such as sensors through human-machine interface software. ...
... It intends to monitor, gather, and process real-time data, while directly interacting with devices such as sensors through human-machine interface software. In addition, it records events into a log file to keep track of each action, to improve the safety and efficiency of port operations by ensuring that everything goes smoothly, and none of the equipment work outside the specified limits (Stouffer and Falco 2006;Sridhar and Manimaran 2010;Inductive Automation 2018). ...
... As such, PLC can help SCADA control port operations, such as collecting data from sensor systems and monitoring port activities for alarm conditions. (Stouffer and Falco 2006). SCADA and PLC can communicate with each other via shared memory. ...
With rapid technological innovations spurred by the Fourth Industrial Revolution (Industry 4.0), today’s seaports are pressured to transform the way they operate in order to handle traffic flows. Such a transformation calls for the development of a smart port system. Despite the growing interest in smart ports, their underlying framework, architecture, and potential ramifications for port productivity are not well documented in the maritime logistics literature. To help port communities better comprehend smart port concepts and successfully develop a smart port within a global supply chain, this paper synthesizes core smart port concepts, designs underlying architecture, and proposes specific milestones for monitoring the smart port development project. We use content analysis and then we identify key success factors (e.g., essential components for the smart port architecture, value propositions, smart port performance metrics) for the establishment and sustainable growth of the smart port. The paper also aims to provide practical guidance for dealing with smart port challenges and opportunities. Our research reveals that a smart port reduces port-user response time, improves port asset utilization, and enhances maritime logistics visibility by automating and integrating end-to-end port operations digitally without human intervention.
