Containerized Testbed Architecture for Cybersecurity Data Collection on Malicious Activities in Industrial Water Systems

The oil and gas industry, a vital lifeline fueling the global economy, find itself at a pivotal juncture where the convergence of operational technology (OT) and information technology (IT) has ushered in unprecedented opportunities and challenges. As digital transformation sweeps across this sector, the imperative to fortify cybersecurity defenses against ever-evolving threats has become paramount. Innovative and forward-looking oil and gas organizations across the globe are adopting the Cloud in many forms because of their digital transformation initiatives. Data lakes, edge technology, machine-to-machine communication, and machine learning (ML) algorithms have been enabling this industrial digital transformation. This transformation is also driving changes to the OT landscape, and as these environments continue to evolve, OT environments are leveraging well proven IT solution patterns to improve the productivity and efficiency of production operations. Industrial customers often start their digital transformation journey by sending OT data to the cloud for analysis and analytics without sending commands back to the industrial automation and control systems (ICAS). This process is often called "open loop" operations, since there is one-way communication from edge to cloud. Customers generally find this relatively easy to secure and manage. However, more often we are witnessing requirements to optimize operations by generating an automatic or operator-initiated response in the oil and gas production operation, rig management based on insights gained from cloud analytics. This process is often referred to as "closed loop" operations with two-way communication between edge and cloud. The security and compliance practices for closed loop operations are more rigorous because closed operations manipulate OT devices remotely. Developing these practices should be rooted in a cyber risk assessment to help businesses understand and prioritize security concerns. In this paper we propose how strengths of Cloud computing can become key enabler for oil and gas organizations in helping them enhance their overall security posture and manage risks within OT environments. We have deployed solution patterns described in this paper as the foundational pillar of several Oil and Gas organization's overall OT system architecture to unlock both "open loop" and "closed loop" operations in a secure, reliable and cost-effective manner. The specific scope items we will cover in this paper focus on a custom security uplift framework having four foundational components – 1) Cloud led architecture patterns to provide next generation network segmentation strategies in OT De-militarized Zone 2) OT asset inventory and vulnerability management 3) Centralized security monitoring and incident response with help of Artificial Intelligence and most recently providing Generative AI based virtual assistant to query security event data from OT systems. For organizations to plan their industrial digital transformation safely and securely, it is recommended that a multi-layered approach to secure the Industrial Control Systems (ICS)/OT and Cloud environments be implemented as captured in ten security golden rules in the following paper [1]. In addition to this, in the paper we propose a comprehensive architecture framework that aligns with established cybersecurity framework (CSF) such as NIST. The use case studies discussed in the paper will highlight how customer have been able to remediate critical security vulnerabilities within weeks post implementation by deploying a comprehensive asset inventory discovery and vulnerability assessment. This has help reduce mean time to identify and mitigate vulnerabilities from months to days with automated testing in pre-production environments, ensure quick detection and response towards security incidents with help of advanced security monitoring and incident response playbooks, covering 100% of the OT assets through this capability and leverage advanced data analytics, machine learning to perform log mining, data cleansing, data validation, log mining and analysis through natural language processing such as conversational AI assistants powered by large language model (LLM) in the Cloud.

As an extension of a previous methodological proposal to provide management of information security in Industrial Control Systems (ICS), this study aims to adapt IT frameworks to protect industrial and manufacturing enterprises against Information and Communication Technology disruptions and malicious activity. In order to accomplish this purpose, the integration of traditional IT standards and good practices such as COBIT, PMI-PMBOK, ITIL and NIST have been merged. Hereby, COBIT has been applied to align management with the enterprise strategy, PMI-PMBOK for project management, and ITIL for the support and maintenance of ICS services. In this respect, NIST-SP 800-82 has been used as a Guide to ICS Security. Prior to its implementation, we performed an evaluation and selection of a group of tools of these frameworks. Furthermore, they have been used effectively in the operational management of the information security in real cases. Among the main obtained benefits, we were able to reduce incidents and accomplished a holistic management. The achieved results and indicators demonstrate that the management tools comply with the control of the information security in the ICS in the contexts of technology, processes, and people aligned with the strategic objectives.

Nuclear power plants are important industrial production facilities whose safety issues can have disastrous consequences for the social environment. With the deep fusion of industrialization and information, the nuclear power control system is gradually transformed into automatic digital systems. Such development improves the production efficiency and economic benefits, but introduce the security problem from traditional IT systems into industrial control systems. However, the existing power industrial control system of nuclear power plant mainly adopts firewall, anti-virus software, network gate and other passive security strategies, which lack the safety of active. Besides, only part of the data can be backed up locally and the remote backup is completed manually by operators regularly. The security of industrial control systems cannot be guaranteed and real-time remote backup cannot be realized. In this paper, we analyze and summarize the characteristics of industrial control systems used in nuclear power plants. Then we propose a Truly Private Network (TPN) for nuclear power plants based on the Multi-identifier network (MIN), which integrates blockchain, cryptography, and other security mechanisms to provide a trusted environment. Lastly, we build a network remote real-time backup scheme based on Mimic distributed storage technology providing data tamper-resistance and traceability for nuclear power plants.

System safety and cyber security have a great effect on system reliability which is the foundation of keeping system running-normally. Although safety and security controls can reinforce each other mutually, contradictions between them also exist, such as resource competition that may cause serious damage. This paper puts forward a risk-based task scheduling approach for the integrated control of system safety and cyber security in industrial control systems (ICSs). In this approach, an integrated task model covering both safety task and security task is proposed, which describes the relationship between task and risk in detail. And the change of real-time risk during the enforcement of these tasks is analysed. These tasks are scheduled through an optimal scheduling scheme which means the risk decrease as soon as possible and is always acceptable based on genetic algorithm. Further, simulation results show that the proposed approach is effective for task scheduling and real-time risk control.

In order to ensure the reliable execution of the functional safety of industrial control systems and effectively resist more and more serious cyber attacks, it is necessary to explore a technical solution suitable for industrial control systems to effectively ensure the compatibility of cyber security and functional security measures. Taking typical SIL3 and SL3 industrial control systems as examples, based on functional safety measures, cyber security measures are analyzed one by one, and potential contradictions between them are identified. Coordination solutions of functional safety and cyber security are given by combining event tree and risk analysis. Finally, integrated protective measures of functional safety and cyber security of industrial control systems are obtained.The results show that the cyber security measures determined by the coordination method are compatible with the original functional safety measures, and the coordination method can be used to guide the cyber security design of industrial control systems.

Adversarial attacks targeting industrial control systems, such as the Maroochy wastewater system attack and the Stuxnet worm attack, have caused significant damage to related facilities. To enhance the security of industrial control systems, recent research has focused on not only improving the accuracy of intrusion detection systems but also developing techniques to generate adversarial attacks for evaluating the performance of these intrusion detection systems. In this paper, we propose a deep reinforcement learning-based adversarial attack framework designed to perform man-in-the-middle attacks on industrial control systems. Unlike existing adversarial attack methods, our proposed adversarial attack scheme learns to evade detection by the intrusion detection system based on both the impact on the target and the detection results from previous attacks. For performance evaluation, we utilized a dataset collected from the secure water treatment (SWaT) testbed. The simulation results demonstrated that our adversarial attack scheme successfully executed man-in-the-middle attacks while evading detection by the rule-based intrusion detection system, which was defined based on the analysis of the SWaT dataset.

The architecture of Trusted Computing 3.0 provides an excellent way to enhance the security of industrial control systems (ICS). However, the implementation of this architecture needs to solve some technical issues such as access control and trust quantification in the ICS scene. This paper proposes an active defense architecture for ICS based on trusted computing 3.0, which defines the active immune operation mechanism of the industrial control system, establishes an immune architecture based on trusted two-node, multi-level fine-grained access control strategy, and the quantification of the trusted behaviors and decisions. Taking the protection of critical data in DCS as an example, we design a deployment scheme of the coordinated protection for ICS. The proposed method will guide the security enhancement based on trusted computing in ICS.

Looking back over papers, publications and standards for the security of Industrial Control Systems (ICSs), it is striking how little human factors featured in the early days. Very extensive discussions of ‘Defence in Depth’ took no real notice of the risk to systems from poor security awareness, lack of training or malicious or casual behaviour. More recently, human factors have been recognised as a significant factor in securing ICS, but even now there has been relatively little consideration of this as much more than a footnote to detailed technical analysis and recommendations. This article examines the key considerations, develops them into some areas which may not be immediately apparent, and highlights the ways in which human factors in ICS security may differ from the security of conventional IT systems.

Information and cyber security of Industrial Control Systems (ICS) has gained considerable importance. Situation Awareness (SA) is an exciting mechanism to achieve the perception, comprehension and projection of the ICS information security status. Based on the Purdue Enterprise Reference Architecture (PERA), a situation awareness framework for ICS is presented considering the ICS cyber kill chain. The proposed framework consists of IT SA Centre, OT SA Centre, and Comprehensive SA Centre. Comprehensive SA Centre is responsible for creating and maintaining an integrated and high level of security visibility into the whole environments. The introduced framework can be used to guide the development of the situation awareness infrastructure in organization with industrial control systems.

The security of Industrial Control Systems (ICS) has been attracting increased attention over the past years, following the discovery of real threats targeting industrial environments. Despite this attention, automation of the reverse engineering process of ICS binaries for programmable logic controllers remains an open problem, mainly due to the use of proprietary compilers by ICS vendors. Such automation could be a double-edged sword; on the one hand it could accelerate digital forensic investigations and incident response actions, while on the other hand it could enable dynamic generation of malicious ICS payloads. In this work, we propose a structured methodology that automates the reverse engineering process for ICS binaries taking into account their unique domain-specific characteristics. We apply this methodology to develop the modular Industrial Control Systems Reverse Engineering Framework (ICSREF), and instantiate ICSREF modules for reversing binaries compiled with CODESYS, a widely used software stack and compiler for PLCs. To evaluate our framework we create a database of samples by collecting real PLC binaries from public code repositories, as well as developing binaries in-house. Our results demonstrate that ICSREF can successfully handle diverse PLC binaries from varied industry sectors, irrespective of the programming language used. Furthermore, we deploy ICSREF on a commercial smartphone which orchestrates and launches a completely automated process-aware attack against a chemical process testbed. This example of dynamic payload generation showcases how ICSREF can enable sophisticated attacks without any prior knowledge.

Industrial control systems (ICS)- the backbone of any modern nation's critical infrastructure (CI) such as the water, energy, transportation and information and communication technology (ICT) sectors-have been subjected to considerable cyberattacks in recent times. This is exacerbated by the gradual convergence of the information technology (IT) and operational technology (OT) domains. This paper sought to establish the basic cyber hygiene practices to stop malicious cyber activities against connected OTs. Cyber hygiene practices refer to knowledge, attitudes and behaviours consistently put into practice to eliminate a few root causes responsible for many cyber incidents. A scoping technique was adopted to review ICS security studies conducted in the past ten years (2012-2022). The key findings indicate that many CI operators have poor password and access control policies for onsite and remote access to networks, do not perform system updates and patching as and when it is necessary to do so, do not actively restrict, segregate and/or separate access to ICS networks and devices, and do not conduct cybersecurity awareness training as frequently as they should. Lastly, due to legacy ICS networks, threat intelligence tools such as extended detection and response and similar others to monitor and detect, in real-time, anomalous behaviour on the networks are rarely utilised for cyber risk assessment on OT networks.

With the rise of Internet of Things technology and smart manufacturing, the issues of cyber security in industrial control systems (ICS) have become of great concern. Critical infrastructures, such as power plants and water treatment facilities, which heavily rely on ICS, have increasingly come under cyberattack in recent years. These cyberattacks pose severe threats to public safety as well as national security. Therefore, protecting ICS from cyberattack is an important research issue in the era of information technology and operation technology convergence. In this study, we propose an anomaly detection method based on semi-supervised techniques for industrial control systems, using the k-means and convolutional autoencoder methods. Experimental results show that the proposed method outperforms the other methods in almost all performance metrics for gas pipeline and water storage tank datasets.

As an important part of guard system, intrusion detection systems have great significance to the security of industrial control systems. Cause the industrial control systems of the on-site environment is often very complicated, the process data extracted on-site have a wide variety of features, and they always have complex associations and are not independent. The intrusion detection algorithms for traditional industrial control systems often do not solve this problem, which results in a decrease in the accuracy, precision and high false negative rate (FNR), false positives rate (FPR). Aiming at solving the current problems, this paper proposes an algorithm combining the Fast Independent Principal Component Analysis (FastICA) and Support Vector Machine (SVM). Finally compared with the SVM and PCA-SVM algorithms, the experimental results show that the accuracy rate has been significantly improved and slight FPR.KeywordsIntrusion detectionFastICASVM

In the last years, cyber security of Industrial Control Systems (ICSs) has become an important issue due to the discovery of sophisticated malware that by attacking Critical Infrastructures, could cause catastrophic safety results. Researches have been developing countermeasures to enhance cyber security for pre-Internet era systems, which are extremely vulnerable to threats. This paper presents the potential opportunities that Software Defined Networking (SDN) provides for the security enhancement of Industrial Control Networks. SDN permits a high level of configuration of a network by the separation of control and data planes. In this work, we describe the affinities between SDN and ICSs and we discuss about implementation strategies.

Modern Industrial Control Systems (ICS) rely on enterprise to plant floor connectivity. Where the size, diversity, and therefore complexity of ICS increase, operational requirements, goals, and challenges defined by users across various sub-systems follow. Recent trends in Information Technology (IT) and Operational Technology (OT) convergence may cause operators to lose a comprehensive understanding of end-to-end data flow requirements. This presents a risk to system security and resilience. Sensors were once solely applied for operational process use, but now act as inputs supporting a diverse set of organisational requirements. If these are not fully understood, incomplete risk assessment, and inappropriate implementation of security controls could occur. In search of a solution, operators may turn to standards and guidelines. This paper reviews popular standards and guidelines, prior to the presentation of a case study and conceptual tool, highlighting the importance of data flows, critical data processing points, and system-to-user relationships. The proposed approach forms a basis for risk assessment and security control implementation, aiding the evolution of ICS security and resilience.