Secure SCADA-IoT Platform for Industrial Automation and Control: A Collaborative-Communication Designed Model

This paper deals with the design and implementation of an internet of things (IoT) based supervisory control and data acquisition (SCADA) system for a multi microgrid system. The individual microgrid in this work is suggested to be consisting of solar photovoltaic (PV) array, battery bank, and diesel generator, in addition to national grid. IoT is used to provide a suitable wireless means to transfer the monitoring and control data between the generation subsystems and the microgrid controller. SCADA architecture that make use of IoT infrastructure enables efficient and reliable power sharing and also give a clear image about the system status and subsystem failure and power shortage. This system gets benefit of various sensors, including current, voltage, power, and temperature sensors, that are used to collect data from of the PV array and battery bank, and also gives clear image about power demand in each microgrid. The microcontroller ESP32 is used as remote terminal unit (RTU) controller in SCADA system in order to get data from sensors as well as to communicate with the master terminal unit (MTU). By using Cayenne remote server IoT platform deploy the message queuing telemetry transport (MQTT) protocol to store, monitor, and manage data from a MTU. The designed system is low-cost and effective system to monitor and control microgrids that contain many distributed generators.

Проаналізовано особливості застосування систем SCADA (Supervisory Control And Data Acquisition) та технологій Інтернету речей в умовах переходу промисловості до Індустрії 4.0. Показано, що використання веб SCADA забезпечує більшість вимог до таких систем та є масштабованим рішенням на всіх її рівнях. Розроблено та реалізовано веб-SCADA, компоненти якої працюють на одноплатних комп’ютерах типу Raspberry Pi, визначені вимоги до технічних характеристик компонентів системи.

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.

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.

The traditional industrial factory automation, the so-called SCADA (Supervisory control and data acquisition), based on OPC (OLE for Process Control) technology. In recent years, under the impact of information technology, OPC technology is now actively updated to the new standard OPC UA (Unified Architecture). SCADA and the Internet of things, in essence, are the data capture and display, there are many overlapping features. Based on the JustIoT Internet of Things system, this paper designs the bridge hardware and software to bring the operation data of old factory automation equipment into the JustIoT with OPC client or the TCP Modbus protocol. The main feature of HMI SCADA is real-time map monitoring and control. This research adds that feature to JustIoT. Tools are added to JustIoT to let users create HMI page, add graphics and animation gif, and set up the association between pictures and data points. Then, remote monitoring and control can be performed. The cloud supervisory control system has been successfully applied in the offshore wind power monitoring.

In supervisory control and data acquisition (SCADA) systems or the Internet of Things (IoT), human machine interface (HMI) performs the function of data acquisition and control, providing the operators with a view of the whole plant and access to monitoring and interacting with the system. The compromise of HMI will result in lost of view (LoV), which means the state of the whole system is invisible to operators. The worst case is that adversaries can manipulate control commands through HMI to damage the physical plant. HMI often relies on poorly understood proprietary protocols, which are time-sensitive, and usually keeps a persistent connection for hours even days. All these factors together make the vulnerability mining of HMI a tough job. In this paper, we present EUFuzzer, a novel fuzzing tool to assist testers in HMI vulnerability discovery. EUFuzzer first identifies packet fields of the specific protocol and classifies all fields into four types, then using a relatively high efficiency fuzzing method to test HMI. The experimental results show that EUFuzzer is capable of identifying packet fields and revealing bugs. EUFuzzer also successfully triggers flaws of actual proprietary SCADA protocol implementation on HMI, which the SCADA software vendor has confirmed that four were zero-day vulnerabilities and has taken measures to patch up.

Internet of Things (IoT) becomes more popular, and things are connected to each other through wired or wireless communication methods. Though things are connected with various methods easily, it attracts network attackers who exploit these open and convenient network connections in order to obtain unjustified information and benefits or to subvert various IoT systems. Especially, Denial of Service (DoS) attack becomes a serious problem on IoT system where huge number of devices are connected to. These devices are usually connected to IoT gateways in order to send packets to Internet. However, currently the impact of DoS attack on an IoT gateway, which has various interfaces such as wireless LAN interface and wired LAN interface, is not well examined. In this paper, we assess the impact of DoS attack on an IoT gateway with various scenarios. We implemented a prototype of an IoT gateway which has wired and wireless network interfaces by using Raspberry Pi, OpenWRT, and OVS (Open vSwitch). With this prototype, we evaluated various DoS attack scenarios on this IoT gateway. Through this evaluation, we observed the severity of DoS attack on IoT gateways, especially for wireless connections.

The advanced connection requirements of industrial automation and control systems have sparked a new revolution in the Industrial Internet of Things (IIoT), and the Supervisory Control and Data Acquisition (SCADA) network has evolved into an open and highly interconnected network. In addition, the equipment of industrial electronic devices has experienced complete systemic integration by connecting with the SCADA network, and due to the control and monitoring advantages of SCADA, the interconnectivity and working efficiency among systems have been tremendously improved. However, it is inevitable that the SCADA system cannot be separated from the public network, which indicates that there are concerns over cyber-attacks and cyber-threats, as well as information security breaches, in the SCADA network system. According to this context, this paper proposes a module based on the token authentication service to deter attackers from performing distributed denial-of-service (DDoS) attacks. Moreover, a simulated experiment has been conducted in an energy management system in the actual field, and the experimental results have suggested that the security defense architecture proposed by this paper can effectively improve security and is compatible with real field systems.

Security of Supervisory Control and Data Acquisition (SCADA) systems remains a vital issue due to the criticality of the systems they control. They are used in various critical infrastructures, including electric power, oil & gas and others. The protocols used in this domain were designed to satisfy operational requirements without much consideration for security issues. Most SCADA protocols in use today have the ability to transport control messages over TCP/IP networks, leaving them open to attacks that may have a catastrophic impact on the physical systems they control. This paper describes a research effort to improve the current state intrusion detection solutions for SCADA systems. Intrusion Detection Systems (IDS) used in this domain are typically extensions of IT tools, such as Snort, that have been extended with generic static signatures. The main limitation of this approach is its inability to adapt to system changes and it only offers partial support for deep-packet inspection. The proposed approach improves on existing IDS solutions by developing a framework that allows (i) dynamic rule generation and (ii) fine-tuned deep-packet inspection. A simple language grammar allows network engineers to describe system characteristics that include network topology, protocol and the roles of the different control nodes. A compiler, instrumented with SCADA-specific semantic rules, recognizes monitoring needs and generates network signatures that are specific to the system monitored by the IDS. An organization using our approach has the ability to adapt to system changes by simply updating the system description and its monitoring needs.

The purpose of this paper is to mapping and review what has been done on the topic of research on predictive maintenance in SCADA (Supervisory Control and Data Acquisition) based industries. In the research area of predictive maintenance, various methods for predicting damage or time to failure of a machine have been proposed and applied in various industries. This paper systematically categorizes predictive maintenance in SCADA-based industries research based on industry classifications according to ISIC (International Standard Industrial Classification of All Economic Activities). Furthermore, the research scope is explored its connection to the topics of Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), and Supervisory Control and Data Acquisition (SCADA). It is found that 81.5% of the research was conducted on the electricity, gas, steam, and air conditioning supply industries, 11.1% of research was conducted on the mining and quarrying industry, and 7.4% of the research conducted in the manufacturing industry. It is also found that 85.2% of studies used AI and ML, 18.5% of the studies used IoT, and 18.5% of research used AI/ML and IoT technology together.

Internet of Things (IoT) are developed rapidly in recent years and more than 50 billion of IoT devices are expected to be deployed worldwide in 2020. How to automatically and securely connect the tremendous number of IoT end devices to Internet is one of critical problems to be addressed. This paper proposes secure and automatic Wi-Fi (Wireless Fidelity) connection mechanisms for connecting IoT end devices and IoT gateways. Our design has the following advantages. First, IoT end devices, once powered on, can automatically connect to an IoT gateway without human intervention. Secondly, the SSID and password for high security strength WPA2 connection are randomly generated to enhance IoT security. Finally, the randomly generated password and SSID are automatically changed every day or when network attacking is detected.

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 percentage integration of photovoltaic (PV) inverters in the field has increased significantly in the past 5 years. Regardless of the size of the PV plants and the inverters (residential vs. commercial), it is becoming crucial that these devices have the capability to communicate with peers (other smart devices) and with components that are at a hierarchy above the inverters (e.g., supervisory control and data acquisition (SCADA) systems, distributed controllers, and data managers). This project aims to develop a standard SCADA software code for inverters’ embedded controllers that will enable interoperability with other components in the system. To achieve this, the code will be developed using two different protocols: Distributed Network Protocol 3 and International Electrotechnical Commission 61850. The developed code is aimed to be deployed in simple embedded controllers. It will be tested in the National Renewable Energy Laboratory’s (NREL’s) Energy Systems Integration Facility. The tested code will then be made available through Triangle MicroWorks’s (TMW’s) software platform. The primary objectives of this project include training the NREL team with TMW’s embedded controller libraries, developing an interoperable communication code for embedded controllers, successfully testing and deploying the code, and demonstrating the newly developed code in a conference.

The operational life of offshore wind turbines is in part driven by the fatigue life of key structural components, such as the substructure. In recent years, fatigue life management of operational assets has become evermore important, as older farms are closing on their design lifetime and newer farms are designed with tighter margins. To support decisions on fatigue lifetime, it is advantageous to monitor the fatigue progression in these structures through SHM. However, a full instrumentation of every asset in a farm to assess the fatigue life of the substructure is considered economically infeasible. The drive for SHM in wind has been accompanied by the increased availability of intelligent data-driven methodologies which have attempted to provide the same information without the need for additional hardware. One such cost-effective approach, as described in [1], uses the available supervisory control and data acquisition (SCADA) systems, coupled with acceleration measurements to predict the fatigue life of an offshore wind turbine. The use of acceleration measurement data has been proven critical for capturing the complex dynamics of offshore wind turbines. In this contribution, we present the results of said data-driven approach using SCADA and Internet of Things (IoT) accelerometer installed at nacelle-level to monitor the fatigue life for the entirety of a real-world offshore wind farm comprised of 23 turbines, with a specific focus on long-term damage equivalent fatigue loads (DEL) estimation [2]. The availability of acceleration measurements for all locations in particular, is fundamental, as to cover all possible differences in natural frequencies between turbines would be nearly impossible if solely relying on wave and tidal data. To achieve this goal, a neural network architecture is used, enhanced by physics-informed learning focused on long-term estimation, trained and validated on so-called fleet-leader (three turbines instrumented with strain gauges, which provide the ground truth). In this study, we pay special attention to the farm-wide validation, cross-validation and extrapolation of these models as well as the performance for different operational conditions. Finally, this study is undertook for a real-world instrumentation setup, unparalleled in its scale, and can thus be indicative of future trends for SHM in offshore wind: farm-wide instrumentation and monitoring of structural health based on acceleration measurements, enabling a greater trustworthiness on reliability and durability estimation over the serviceable lifetime.