Biblio

The increasing using of IoT technologies in the industrial sector creates new challenges for the information security of such systems. Using IoT-devices for building SCADA systems cause standard protocols and public networks for data transmitting. Commercial off-the-shelf devices and systems are a new base for industrial control systems, which have high-security risks. There are some useful models are exist for security analysis of information systems, but they do not take into account IoT architecture. The nested attributed metagraph model for the security of IoT-based solutions is proposed and discussed.

Various Machine Learning techniques are considered to be "black-boxes" because of their limited interpretability and explainability. This cannot be afforded, especially in the domain of Cyber-Physical Systems, where there can be huge losses of infrastructure of industries and Governments. Supervisory Control And Data Acquisition (SCADA) systems need to detect and be protected from cyber-attacks. Thus, we need to adopt approaches that make the system secure, can explain predictions made by model, and interpret the model in a human-understandable format. Recently, Autoencoders have shown great success in attack detection in SCADA systems. Numerous interpretable machine learning techniques are developed to help us explain and interpret models. The work presented here is a novel approach to use techniques like Local Interpretable Model-Agnostic Explanations (LIME) and Layer-wise Relevance Propagation (LRP) for interpretation of Autoencoder networks trained on a Gas Pipelines Control System to detect attacks in the system.

An open source and low-cost Supervisory Control and Data Acquisition System based on Node-RED and Arduino microcontrollers is presented in this paper. The system is designed for monitoring, supervision, and remotely controlling motors and sensors deployed for oil and gas facilities. The Internet of Things (IoT) based SCADA system consists of a host computer on which a server is deployed using the Node-RED programming tool and two terminal units connected to it: Arduino Uno and Arduino Mega. The Arduino Uno collects and communicates the data acquired from the temperature, flowrate, and water level sensors to the Node-Red on the computer through the serial port. It also uses a local liquid crystal display (LCD) to display the temperature. Node-RED on the computer retrieves the data from the voltage, current, rotary, accelerometer, and distance sensors through the Arduino Mega. Also, a web-based graphical user interface (GUI) is created using Node-RED and hosted on the local server for parsing the collected data. Finally, an HTTP basic access authentication is implemented using Nginx to control the clients' access from the Internet to the local server and to enhance its security and reliability.

Security maintenance of Supervisory Control and Data Acquisition (SCADA) systems has been a point of interest during recent years. Numerous research works have been dedicated to the design of intrusion detection systems for securing SCADA communications. Nevertheless, these data-driven techniques are usually dependant on the quality of the monitored data. In this work, we propose a novel feature selection approach, called MSFS, to tackle undesirable quality of data caused by feature redundancy. In contrast to most feature selection techniques, the proposed method models each class in a different subspace, where it is optimally discriminated. This has been accomplished by resorting to ensemble learning, which enables the usage of multiple feature sets in the same feature space. The proposed method is then utilized to perform intrusion detection in smaller subspaces, which brings about efficiency and accuracy. Moreover, a comparative study is performed on a number of advanced feature selection algorithms. Furthermore, a dataset obtained from the SCADA system of a gas pipeline is employed to enable a realistic simulation. The results indicate the proposed approach extensively improves the detection performance in terms of classification accuracy and standard deviation.

A Supervisory Control and Data Acquisition (SCADA) system used in controlling or monitoring purpose in industrial process automation system is the process of collecting data from instruments and sensors located at remote sites and transmitting data at a central site. Most of the existing works on SCADA system focused on simulation-based study which cannot always mimic the real world situations. We propose a novel methodology that analyzes SCADA logs on offline basis and helps to detect process-related threats. This threat takes place when an attacker performs malicious actions after gaining user access. We conduct our experiments on a real-life SCADA system of a Power transmission utility. Our proposed methodology will automate the analysis of SCADA logs and systemically identify undesired events. Moreover, it will help to analyse process-related threats caused by user activity. Several test study suggest that our approach is powerful in detecting undesired events that might caused by possible malicious occurrence.

Recent cyberattacks targeting Supervisory Control and Data Acquisition (SCADA)/Industrial Control System(ICS) exploit weaknesses of host system software environment and take over the control of host processes in the host of the station network. We analyze the attack path of these attacks, which features how the attack hijacks the host in the network and compromises the operations of field device controllers. The paper proposes a host-based protection method, which can prevent malware penetration into the process memory by code injection attacks. The method consists of two protection schemes. One is to prevent file-based code injection such as DLL injection. The other is to prevent fileless code injection. The method traces changes in memory regions and determine whether the newly allocated memory is written with malicious codes. For this method, we show how a machine learning method can be adopted.

Supervisory Control and Data Acquisition (SCADA) systems have been a frequent target of cyberattacks in Industrial Control Systems (ICS). As such systems are a frequent target of highly motivated attackers, researchers often resort to intrusion detection through machine learning techniques to detect new kinds of threats. However, current research initiatives, in general, pursue higher detection accuracies, neglecting the detection of new kind of threats and their proposal detection scope. This paper proposes a novel, reliable host-based intrusion detection for SCADA systems through the Operating System (OS) diversity. Our proposal evaluates, at the OS level, the SCADA communication over time and, opportunistically, detects, and chooses the most appropriate OS to be used in intrusion detection for reliability purposes. Experiments, performed through a variety of SCADA OSs front-end, shows that OS diversity provides higher intrusion detection scope, improving detection accuracy by up to 8 new attack categories. Besides, our proposal can opportunistically detect the most reliable OS that should be used for the current environment behavior, improving by up to 8%, on average, the system accuracy when compared to a single OS approach, in the best case.

With the increase of the information level of SCADA system in recent years, the attacks against SCADA system are also increasing. Therefore, more and more scholars are beginning to study the safety of SCADA systems. Game theory is a balanced decision involving the main body of all parties. In recent years, domestic and foreign scholars have applied game theory to SCADA systems to achieve active defense. However, their research often focuses on the entire SCADA system, and the game theory is solved for the entire SCADA system, which is not flexible enough, and the calculation cost is also high. In this paper, a dynamic local game model (DLGM) for power SCADA system is proposed. This model first obtains normal data to form a whitelist, then dynamically detects each attack of the attacker's SCADA system, and through white list to determine the node location of the SCADA system attacked by the attacker, then obtains the smallest system attacked by SCADA system, and finally performs a local dynamic game algorithm to find the best defense path. Experiments show that DLGM model can find the best defense path more effectively than other game strategies.

Cybersecurity of the supervisory control and data acquisition (SCADA) system, which is the key component of the cyber-physical systems (CPS), is facing big challenges and will affect the reliability of the smart grid. System reliability can be influenced by various cyber threats. In this paper, the reliability of the electric power system considering different cybersecurity issues in the SCADA system is analyzed by using Semi-Markov Process (SMP) and mean time-to-compromise (MTTC). External and insider attacks against the SCADA system are investigated with the SMP models and the results are compared. The system reliability is evaluated by reliability indexes including loss of load probability (LOLP) and expected energy not supplied (EENS) through Monte Carlo Simulations (MCS). The lurking threats of the cyberattacks are also analyzed in the study. Case studies were conducted on the IEEE Reliability Test System (RTS-96). The results show that with the increase of the MTTCs of the cyberattacks, the LOLP values decrease. When insider attacks are considered, both the LOLP and EENS values dramatically increase owing to the decreased MTTCs. The results provide insights into the establishment of the electric power system reliability enhancement strategies.

Supervisory Control and Data Acquisition (SCADA)networks are widely deployed in modern industrial control systems (ICSs)such as energy-delivery systems. As an increasing number of field devices and computing nodes get interconnected, network-based cyber attacks have become major cyber threats to ICS network infrastructure. Field devices and computing nodes in ICSs are subjected to both conventional network attacks and specialized attacks purposely crafted for SCADA network protocols. In this paper, we propose a deep-learning-based network intrusion detection system for SCADA networks to protect ICSs from both conventional and SCADA specific network-based attacks. Instead of relying on hand-crafted features for individual network packets or flows, our proposed approach employs a convolutional neural network (CNN)to characterize salient temporal patterns of SCADA traffic and identify time windows where network attacks are present. In addition, we design a re-training scheme to handle previously unseen network attack instances, enabling SCADA system operators to extend our neural network models with site-specific network attack traces. Our results using realistic SCADA traffic data sets show that the proposed deep-learning-based approach is well-suited for network intrusion detection in SCADA systems, achieving high detection accuracy and providing the capability to handle newly emerged threats.

Hybrid attack graphs are a powerful tool when analyzing the cybersecurity of a cyber-physical system. However, it is important to ensure that this tool correctly models reality, particularly when modelling safety-critical applications, such as a nuclear reactor. By automatically verifying that a simulation reaches the state predicted by an attack graph by analyzing the final state of the simulation, this verification procedure can be accomplished. As such, a mechanism to estimate if a simulation reaches the expected state in a hybrid attack graph is proposed here for the nuclear reactor domain.