English

Abstract: In the oil and gas sector, supervisory control and data acquisition (SCADA) systems have become a revolutionary force that are transforming operations throughout the whole value chain. This in-depth analysis looks at the development, design, and various uses of SCADA systems in the upstream, middle, and downstream domains. It projects that the global SCADA market for oil and gas will grow to $4.52 billion by 2026. Real-time well monitoring, production optimization, and remote operations are made possible by SCADA systems in upstream operations, which greatly increase output and efficiency. The goal of midstream applications is to increase pipeline efficiency and safety by using predictive maintenance, enhanced leak detection, and flow optimization. By streamlining quality assurance, energy management, and process control, SCADA systems can completely transform refinery operations. SCADA deployments are not without difficulties, though, including data management complications, cybersecurity threats, and legacy system interoperability. In addition to addressing these problems, the study looks at new developments that could improve SCADA capabilities, such as edge computing, digital twins, AI and machine learning integration, and 5G technology. According to the research, SCADA is essential for promoting efficiency, safety, and innovation in the oil and gas industry. Businesses that successfully integrate SCADA technologies while overcoming implementation obstacles will be best positioned to prosper in the complicated and fiercely competitive energy market.

Supervisory Control and Data Acquisition (SCADA) is used in modern power system for monitoring, controlling, gathering the information, transfer to information back to a central station, carry out necessary analysis and control carry out necessary analysis and control. A SCADA system involves the integrated technologies of computer, communication, electric devices etc. Here the communication media provides the physical medium for transferring, controlling and acquisition of data within SCADA from remote locations. Although wireless communications media become an attractive option as communications networks for SCADA system, compared with wired communications, wireless communications in power system environments presents a number of security and reliability issues. As in SCADA system the communication media plays an important role, to meet objectives of the SCADA systems, different wired and wireless communications media have been applied in modern power system environment. This paper presents a concept about SCADA system using Cognitive Radio based IEEE 802.22 standard for overcoming all existing limitations of SCADA's communication media for future power system where details design, analyses of feasibility and benefits over wired and other wireless systems are also described.

SCADA (supervisory control and data acquisition) systems are used to monitor and control the processes of industrial facilities remotely. The use of standard technologies and interconnections between the systems lead to variety of security attacks. SCADA systems are being the part of many critical applications of the society. Any minute deviation in the normal operation of the system results in serious consequences. Hence, securing the industrial control systems is a high priority issue. One can provide security and safety to the system by identifying possible sources of threats and objectives of attackers and continuous monitoring of the operations of the system. In this paper, attack incidents occurred on command and control systems are presented (from the year 1982 to 2017), the general attacker goals on SCADA systems are discussed, SCADA communication protocols and its normal/abnormal behaviors are analyzed using the Wireshark tool.

In view of the insufficient informationization of the stainless steel pipe production line., such as the inability to collect the machine tool's processing data in real time, the inaccurate production information feedback, and the insufficient monitoring of the equipment status, a SCADA (Supervisory Control And Data Acquisition) system is designed to effectively control and monitor the entire stainless steel pipe production line to achieve the information construction of the factory. Siemens S7-1500 series PLC is used as the lower computer of this system, and communicates with the upper computer software through Siemens S7 protocol. The SCADA system is developed in C# language, and its functions include equipment status monitoring, production process monitoring, production statistical analysis and electronic board display. The SCADA system can realize the automation of data collection, data processing and data analysis, which improves the production efficiency and management level of the production line.

This paper proposes an intelligent gateway system for SCADA networks to avoid DOS attacks. The proposed approach provides details about an SIG system architecture which establishes the connection between Master SIGs and Perimeter SIGs, the traffic flow, major alerts, and minor alerts. Simulation experiments are an indispensable phase to analyze and assess the security of SCADA (Supervisory Control and Data Acquisition) systems. Although numerous experiments have taken place, limitations are still not been shrunk. The SCADA Intelligence Gateway concept proposed in this paper is experimentally proved and showed its ability to secure the SCADA system from attacks. The intelligence learning mechanism established the ability to identify malicious traffic through bot production and simulation environments.

Recently, as a demand for connecting Supervisory Control and Data Acquisition (SCADA) systems to open networks increases, the study of SCADA system security becomes an issue. Many researchers have proposed key management schemes for SCADA systems. However, previous studies lack the proper considerations for availability. In this paper, we build up cryptographic security requirements for robust SCADA systems. In addition, we propose a hybrid key management architecture for robust SCADA systems which supports replace protocol for availability and reduces the number of keys to be stored in a master terminal unit.

The supervisory control and data acquisition (SCADA) systems monitor and control industrial control systems in many industrial and economic sectors such as water treatment, power plants, railroads, and gas pipelines. The integration of SCADA systems with the internet and corporate enterprise networks for various economical reasons exposes SCADA systems to attacks by hackers who could remotely exploit and gain access to SCADA systems to damage the infrastructure and thereby harming people’s lives. The simplicity of datasets and possible overfitting of models to training data are some of the issues in the previous research. In this paper, we present detecting and classifying malicious command and response packets in a SCADA network by analyzing attribute differences and history of packets using k-means clustering. This study presents a solution to classify SCADA cyber attacks to detect and classify SCADA attacks with high accuracy using a big data framework that comprises of Apache Hadoop and Apache Mahout. Apache Mahout’s random forest classification algorithm is applied on SCADA’s gas pipeline dataset to categorize attacks. When 70% of the data is used for training the classifier, our approach resulted in 5–17% improvement in accuracy for the classification of read response attacks and 2–8% improvement in accuracy for write command attacks with respect to using the original dataset.

In the modern industrial control landscape, Supervisory Control and Data Acquisition (SCADA) systems serve as critical intermediaries in automating and managing complex processes across many industries, including energy, manufacturing, and utilities. While maintaining the availability and efficiency of these systems is essential, their increased integration into networked environments has exposed them to a growing array of cyber threats. As SCADA systems continue to grow and be deployed globally, malicious actors are increasingly target critical infrastructure, making security management progressively more challenging. Our research explores the benefits of conducting forensic investigations focused on SCADA Human-Machine Interface (HMI) systems, emphasizing how centralized equipment analysis can help overcome the unique challenges these systems present. To assist investigators in efficiently collecting system-wide data from SCADA HMI environments, we developed a forensic analysis framework that enables the evaluation of system discrepancies and evidence of potential cyberattacks. Additionally, we present a comprehensive forensic analysis of a SCADA testbed featuring Ignition, a popular SCADA software platform developed by Inductive Automation. Our investigation reveals that SCADA HMI forensics can provide valuable insight into the system's state, allowing for the identification and cross-analysis of critical equipment and operational data across memory, network, and disk. This framework is further supported by the creation of the Ignition Forensic Artifact Carving Tool (IFACT), a SCADA HMI Forensic Analysis Tool designed to assist in extracting artifacts and parsing system information from forensic data sourced from Ignition HMIs. Using IFACT, we achieve accurate insights into device and equipment data in SCADA systems, investigating data persistence in volatile memory and the variability of its lifetime based on the system state.

The signal of effective wind speed plays an important role in the process control of Wind Turbine Generation System (WTGS). Due to inaccurate measurement of it, this paper proposes a useful procedure to complete effective wind speed estimation online combining mechanism inference and adaptive data processing algorithm. The historical data in Supervisory Control and Data Acquisition (SCADA) system is used and the Adaptive Neuro-Fuzzy Inference System (ANFIS) is adopted. Moreover, by analyzing the operation of WTGS, a procedure is suggested to establish the aerodynamic characteristic related to steady operation points which can be obtained by K-means clustering from SCADA system. Then, according to the effective wind speed, an optimum setting strategy is formulated. The validation is executed and the simulation results show the availability of the approaches.

Critical infrastructure such as power plants, oil refineries, and sewage are at the core of modern society. Supervisory Control and Data Acquisition (SCADA) systems were designed to allow human operators supervise, maintain, and control critical infrastructure. Recent years has seen an increase in connectivity of SCADA systems to the Internet. While this connectivity provides an increased level of convenience, it also increases their susceptibility to cyber-attacks. Given the potentially severe ramifications of exploiting SCADA systems, the purpose of this study is to utilize passive and active vulnerability assessment techniques to identify the vulnerabilities of Internet enabled SCADA systems. Specifically, we collect a large testbed of SCADA devices from Shodan, a search engine for the IoT, and assess their vulnerabilities with Nessus and against the National Vulnerability Database (NVD). Results of this study indicate that many SCADA systems from major vendors such as Rockwell Automation and Siemens are vulnerable to default credential, man-in-the-middle, and SSH exploit attacks.

Supervisory Control and Data Acquisition (SCADA) systems control and monitor industrial and critical infrastructure functions, such as the electricity, gas, water, waste, railway and traffic. Recently, SCADA systems have been targeted by an increasing number of attacks from the Internet due to its grow- ing connectivity to Enterprise networks. Traditional techniques and models of identifying attacks, and quantifying its impact cannot be directly applied to SCADA systems because of their limited resources and real-time operating characteristics. The paper introduces a novel framework for evaluating survivability of SCADA systems from a service-oriented perspective. The framework uses an analytical model to evaluate the status of services performance and the survivability of the overall system using queuing theory and Bayesian networks. We further discuss how to learn from historical or simulated data automatically for building the conditional probability tables and the Bayesian networks.

Most of the machinery and equipment of the mine infrastructure is controlled by an industrial automation system. In practice, SCADA (Supervisory Control And Data Acquisition) systems very often acquire many operational parameters that have no further analytical use. The variability of recorded signals very often depends on the machine load, as well as organizational and technical aspects. Therefore, SCADA systems can be a practically free source of information used to determine KPI (e.g. performance, energy and diagnostic) for a single object as well as given mining process. For example, the ability to reliably identify different operational modes of the mining industrial fans based on data from SCADA gives a wide range of potential applications. Accurate information on this subject could be used - apart from basic monitoring and reporting needs (e.g. actual work vs. schedule comparison) – also in more complex problems, like power consumption predictions. Given the variety of industrial fans used in the mining industry and the different operational data collected, this is yet not a trivial task in a general case. The main aim of this article is to provide reliable algorithms solving fans operational mode identification issue, which will be possible to apply in a wide range of potential applications.

The convergence of Supervisory Control and Data Acquisition (SCADA) systems with the Internet of Things (IoT) and Machine Learning (ML) is redefining automation, security, and operational efficiency across industries. Traditional SCADA systems, widely used in critical infrastructure such as energy, water management, and industrial processes, are undergoing a transformative shift with the integration of IoT. IoT-enabled sensors and devices provide real-time data streams from diverse operational environments, enabling SCADA systems to achieve enhanced situational awareness and remote monitoring capabilities. Machine Learning further augments SCADA systems by introducing advanced analytics for predictive maintenance and anomaly detection. ML algorithms analyse vast datasets collected through IoT devices to forecast system failures, optimize resource utilization, and ensure uninterrupted operations. This predictive approach minimizes downtime, reduces maintenance costs, and improves overall efficiency. Cloud-driven security frameworks are pivotal in addressing the growing cybersecurity challenges associated with SCADA and IoT integration. These frameworks provide scalable and resilient solutions for protecting data integrity, preventing unauthorized access, and mitigating cyber threats. The deployment of ML-driven cybersecurity solutions enhances the ability to detect and respond to sophisticated attacks targeting SCADA environments. This paper examines the evolution of SCADA systems in the era of IoT, focusing on the transformative role of ML and cloud-driven security. It explores innovative applications, highlights the challenges of integrating these technologies, and provides insights into future advancements for creating robust and secure automated systems.

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.

PPFSCADA: Privacy preserving framework for SCADA data publishing