Cyber-physical systems (CPS) are interconnected architectures that employ analog and digital components as well as communication and computational resources for their operation and interaction with the physical environment. CPS constitute the backbone of enterprise (e.g., smart cities), industrial (e.g., smart manufacturing), and critical infrastructure (e.g., energy systems). Thus, their vital importance, interoperability, and plurality of computing devices make them prominent targets for malicious attacks aiming to disrupt their operations. Attacks targeting cyber-physical energy systems (CPES), given their mission-critical nature within the power grid infrastructure, can lead to disastrous consequences. The security of CPES can be enhanced by leveraging testbed capabilities in order to replicate and understand power systems operating conditions, discover vulnerabilities, develop security countermeasures, and evaluate grid operation under fault-induced or maliciously constructed scenarios. Adequately modeling and reproducing the behavior of CPS could be a challenging task. In this paper, we provide a comprehensive overview of the CPS security landscape with an emphasis on CPES. Specifically, we demonstrate a threat modeling methodology to accurately represent the CPS elements, their interdependencies, as well as the possible attack entry points and system vulnerabilities. Leveraging the threat model formulation, we present a CPS framework designed to delineate the hardware, software, and modeling resources required to simulate the CPS and construct high-fidelity models that can be used to evaluate the system’s performance under adverse scenarios. The system performance is assessed using scenario-specific metrics, while risk assessment enables the system vulnerability prioritization factoring the impact on the system operation. The overarching framework for modeling, simulating, assessing, and mitigating attacks in a CPS is illustrated using four representative attack scenarios targeting CPES. The key objective of this paper is to demonstrate a step-by-step process that can be used to enact in-depth cybersecurity analyses, thus leading to more resilient and secure CPS.

/pdf/cyber-physical-energy-systems-security-threat-modeling-risk-i4m2rebetp.pdf

Cyber-Physical Energy Systems Security: Threat Modeling, Risk Assessment, Resources, Metrics, and Case Studies

During the past two decades, oil and gas operational and information technology systems have experienced constant digital growth, closely followed by an increasing number of cyber-attacks on the newly interconnected systems. Adversaries exploit vulnerable accessible device or malware attacks networked services, in an attempt to gain access to critical systems and machinery that are interconnected over networks. Given the importance of the oil and gas sector on the global economy and the diversity of critical systems often being controlled over remote locations, it is highly important to understand and mitigate such attacks. In this paper, we survey cyber-attacks on all three domains of the oil and gas sector (upstream, midstream, downstream) starting from the early 90s up until 2020. For each domain, we document and analyze verified attacks based on real-world reports and published demo attacks on systems. We map and catalogue the attack types used in each case, in order to understand common and subliminal attack paths against oil and gas critical operations. Our aim is threefold, i.e., first, to assess documented attacks using standardized impact assessment techniques and highlight potential consequences of cyber-attacks on this sector, second, to build a vulnerability taxonomy based on technical knowledge gathered by all such incidents and connect each vulnerability with oil and gas systems and respective attack paths, and third, to map the documented knowledge and taxonomies with MITRE’s international knowledge base of Adversary Tactics and Techniques, so as to provide a general guide for analyzing and protecting against cyber-attacks at oil and gas infrastructures.

/pdf/cyber-attacks-on-the-oil-gas-sector-a-survey-on-incident-4nm619h80h.pdf

Cyber-Attacks on the Oil & Gas Sector: A Survey on Incident Assessment and Attack Patterns

Industrial Control Systems (ICSs) are responsible for the automation of different processes and the overall control of systems that include highly sensitive potential targets such as nuclear facilities, energy-distribution, water-supply, and mass-transit systems. Given the increased complexity and rapid evolvement of their threat landscape, and the fact that these systems form part of the Critical National infrastructure (CNI), makes them an emerging domain of conflict, terrorist attacks, and a playground for cyberexploitation. Existing layered-defence approaches are increasingly criticised for their inability to adequately protect against resourceful and persistent adversaries. It is therefore essential that emerging techniques, such as orthogonality, be combined with existing security strategies to leverage defence advantages against adaptive and often asymmetrical attack vectors. The concept of orthogonality is relatively new and unexplored in an ICS environment and consists of having assurance control as well as functional control at each layer. Our work seeks to partially articulate a framework where multiple functional and assurance controls are introduced at each layer of ICS architectural design to further enhance security while maintaining critical real-time transfer of command and control traffic.

Preliminaries of Orthogonal Layered Defence Using Functional and Assurance Controls in Industrial Control Systems

http://manuscript.elsevier.com/S0951832022002186/pdf/S0951832022002186.pdf

Model-based IDS design for ICSs

In existing literature, various machine learning models have been applied to detect cyber attacks on the power grid. None of them, however, consider the degradation of the model over time due to the distributed and dynamic nature of the power system. At the same time, they also fail to recognize natural events, such as line maintenance, since they are based on binary classification (attack/no attack). In an effort to develop a cyber security protection strategy that will work robustly for an extended period of time, we develop a methodology based on the LightGBM framework, which performs well for a) Training for multi-class events (no attack/natural event/attack), and b) Fast, dynamic retraining with concept drift detection. We use an ensemble learning-based classifier for classifying the events generated through our Real Time Digital Simulatorwith commercial devices in a Hardware-in-The-Loop setup. The proposed novel classification model outperforms binary classifier-based approaches, resulting in an over 97% effectiveness with the inclusion of concept drift detection and retraining.

Towards Robust Power Grid Attack Protection using LightGBM with Concept Drift Detection and Retraining

Industrial Control Systems (ICS) are under modernization towards increasing efficiency, reliability, and controllability. Despite the numerous benefits of interconnecting ICS components, the wide adoption of Information Technologies (IT) has introduced new security challenges and vulnerabilities to industrial processes, previously obscured by the systems' custom designs. Towards securing the backbone of critical infrastructure, selection of the proper assessment environment for performing cyber-security assessments is crucial. In this paper, we present a layered analysis of vulnerabilities and threats in ICS components, that identifies the need for including real hardware components in the assessment environment. Moreover, we advocate the suitability of Hardware-In-The-Loop testbeds for ICS cyber-security assessment and present their advantages over other assessment environments.

Enabling multi-layer cyber-security assessment of Industrial Control Systems through Hardware-In-The-Loop testbeds

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Enabling multi-layer cyber-security assessment of Industrial Control Systems through Hardware-In-The-Loop testbeds