Reliability Of Critical Infrastructures Research Articles

Report on the 5th International Workshop on Engineering and Cybersecurity of Critical Systems and 2nd International Workshop on Software Vulnerability Management (EnCyCriS/SVM - 2024)

Increasing system interconnectivity, decentralization, and introduction of new, more intelligent technologies, result in critical infrastructures becoming exposed to increased risk of cyber, physical, and combine cyber-physical attacks. Cyber-attacks on critical systems can inflict severe consequences on to people, society, economy, and national security, and can have adverse effects on safety and reliability of critical infrastructures. The joint EnCyCriS-SVM workshop facilitates discourse and discussion among researchers, practitioner, and students who are working on challenges and solutions related to the industrial revolution. Focus is given on sharing industry experience and project results pertaining to cyber threats on critical systems; secure software engineering; and attack detection and response mechanisms.

Temperature-dependent hydrogen-induced crack propagation behaviour and mechanism in polycrystalline α-iron: Insights from molecular dynamics simulations

Understanding the interactions between hydrogen and material integrity in polycrystalline α-Fe is essential for advancing the reliability of critical infrastructure and energy systems. In this study, molecular dynamics simulations were implemented to pinpoint the crack propagation behaviour and mechanism in polycrystalline α-Fe under various hydrogen concentrations and temperatures. The results show that a phase transition from body-centred cubic to face-centred cubic structure first occurs at the crack tip, followed by grain boundary-mediated plasticity activities at room temperature devoid of hydrogen. A limited amount of hydrogen atoms (H/Fe atomic ratio<1%) induces twinning emission from the tip, and increasing temperature further enhances dislocation plasticity as a consequence of decreased unstable stacking fault energy, thereby leading to the blunting of the crack tip. At high hydrogen concentrations (H/Fe atomic ratio>1%), the formed hydrides ahead of the crack tip suppress the phase transition, and concurrently temperature-enhanced dislocation plasticity disappears. As a consequence, the crack propagation proceeds via grain boundary cavity nucleation and growth, and ultimately evolves into intergranular fracture. These findings provide an atomistic-level explanation for temperature-dependent hydrogen-crack interaction mechanisms, and reveal a transition in the fracture mode from ductile transgranular to intergranular failure associated with locally high hydrogen concentrations found in the experiments.

Convolutional Neural Networks Based Detection System for Cyber-attacks in Industrial Control Systems

Convolutional Neural Networks (CNNs) have emerged as a powerful tool for various pattern recognition tasks, including the detection of cyber-attacks in Industrial Control Systems (ICS). This paper presents a CNN-based detection system specifically designed to safeguard ICS from sophisticated cyber threats. The proposed system leverages the capability of CNNs to automatically learn and extract high-level features from raw data, enabling it to identify anomalies indicative of cyber-attacks with high accuracy. Utilizing a comprehensive cyber-attack dataset containing 59 different types of attacks, the system is trained to distinguish between normal and malicious traffic effectively. Based on the extensive dataset, this study demonstrates that the CNN-based detection system achieves a detection rate of 98.5% and a false-positive rate of 1.2%, significantly outperforming traditional methods. The high detection rate indicates the system's ability to accurately identify a wide range of attack types, while the low false-positive rate ensures minimal disruption to normal operations due to incorrect alerts. These results underscore the system's robustness and reliability in identifying subtle and complex attack patterns. Moreover, the CNN-based system is designed to adapt to new types of attacks as it continues to learn from updated datasets, making it a scalable and future-proof solution. Implementing this system can significantly enhance the cybersecurity posture of industrial environments by providing real-time monitoring and rapid response capabilities. The CNN-based detection system offers a significant advancement in ICS cybersecurity. Effectively identifying and mitigating cyber-attacks contributes to the resilience and reliability of critical infrastructure. The proposed approach improves security measures and ensures industrial processes' continuous and safe operation, which is vital for economic stability and public safety.

Anomaly Detection with Gradient Boosting Regressor on HVAC Systems

HVAC systems are important in buildings due to their significant energy consumption, impact on indoor air quality, and role in occupant comfort. Optimizing the operation and control of these systems is crucial for improving energy efficiency and reducing costs. Anomaly detection in HVAC systems aims to optimize energy consumption, improve thermal comfort and indoor air quality, detect and isolate sensor faults, and, more importantly, detect cyber-attacks. By analyzing system data for unusual patterns or unauthorized access attempts, anomaly detection can play a vital role in safeguarding HVAC systems against cyber threats. Detecting and isolating potential cyber-attacks can prevent disruptions in building operations, protect sensitive data, and ensure the continued functionality of HVAC systems securely and reliably. In this study, Gradient Boosting Regressor is used to improve the anomaly detection capabilities of HVAC systems. Traditional anomaly detection methods often struggle to adapt to the dynamic nature of HVAC systems and may generate false alarms or miss critical issues. To address these challenges, we propose the application of Gradient Boosting Regressor, a powerful machine learning technique, to enhance anomaly detection accuracy and reliability. We evaluate the model's performance using real-world HVAC data, comparing it with existing anomaly detection methods. The results demonstrate significant improvements in the system's ability to identify anomalies accurately while minimizing false alarms. This research advances HVAC system security by providing a more robust and adaptive anomaly detection solution. Integrating Gradient Boosting Regressor into the cybersecurity framework of HVAC systems offers improved protection against cyber threats, thereby enhancing the resilience and reliability of critical infrastructures.

Application of Artificial Intelligence (AI) to Enhance Satellite Security

The security of satellite systems is paramount for maintaining the integrity and reliability of critical infrastructure and national defense. This paper explores the application of artificial intelligence (AI) to enhance satellite security. AI technologies can significantly improve anomaly detection, cyber security, signal jamming mitigation, space situational awareness, predictive maintenance, secure communication, and autonomous satellite operations. By leveraging machine learning algorithms, AI can detect and respond to cyber threats in real-time, differentiate between benign and malicious signal interference, and predict potential system failures. Additionally, AI enhances space situational awareness by processing vast amounts of sensor data to track objects in space and predict collision risks. The integration of AI with encryption techniques and block chain technology further strengthens the security framework, ensuring robust and tamper-proof satellite communications. This multidisciplinary approach demonstrates the potential of AI to provide dynamic and proactive defense mechanisms, ensuring the continuous and secure operation of satellite systems in an increasingly complex and threat-prone environment.

REVIEW ON NON-DESTRUCTIVE TECHNIQUES FOR EARLY FLAW DETECTION IN INSPECTIONS

Non-destructive testing (NDT) is critical in ensuring various materials’ and structures’ integrity and safety by detecting flaws without causing damage. Early flaw detection is essential for preventing catastrophic failures, reducing maintenance costs, and enhancing operational efficiency. This research paper comprehensively reviews non-destructive techniques for early flaw detection in inspections. The paper begins with an introduction to the significance of flaw detection and the motivation behind employing non-destructive techniques. It proceeds with a thorough literature review, summarizing existing research and highlighting the strengths and limitations of different methods. The main section of the paper delves into the description and principles of common non-destructive techniques, including Ultrasonic Testing (UT), Radiographic Testing (RT), Magnetic Particle Testing (MPT), Liquid Penetrant Testing (LPT), Eddy Current Testing (ECT), Thermography, Acoustic Emission Testing (AET), and others. The efficacy of each technique is explored through various case studies and real-world applications across diverse industries. Advantages, limitations, and factors influencing technique selection in specific scenarios are critically analyzed. Furthermore, the paper discusses recent advancements and emerging technologies in non-destructive testing that offer potential improvements for early flaw detection. It also covers relevant international and industry-specific standards and regulations governing NDT, emphasizing their significance in enhancing inspection reliability. The conclusion reaffirms the importance of non-destructive techniques for early flaw detection. It proposes future research directions to overcome existing challenges. This review aims to provide engineers, researchers, and practitioners with valuable insights into the state-of-the-art NDT methods, fostering continuous improvement in early flaw detection and ensuring the safety and reliability of critical infrastructures and materials.

Entropy-driven Monte Carlo simulation method for approximating the survival signature of complex infrastructures

The reliability of critical infrastructures, such as power distribution networks, is of key importance for modern societies. The reliability of such complex systems can, in principle, be assessed by Monte Carlo simulation. However, the size and complexity of these systems, and the rarity of the failure events, can make the calculations quite demanding. Survival signature can help to address this issue, as it allows modelling the structure of the system separately from the probabilistic modelling for the reliability assessment. However, the survival signature calculation of complex, multi-component systems for their reliability assessment suffers from the curse of dimensionality, and both analytical calculation and Monte Carlo Simulation (MCS) are not feasible in practice. Then, in this work, we propose a novel approach to approximate the survival signature of a system, which stands on the use of entropy to drive the sampling by MCS towards non-trivial system structure configurations, so as to save computational cost. The approach is exemplified by calculating the reliability of a generic synthetic multi-component network and the feasibility of its application is shown on a real-world network.

Damage Detection of a Pressure Vessel with Smart Sensing and Deep Learning

Structural Health Monitoring plays a crucial role in ensuring the safety and reliability of critical infrastructure, including pressure vessels involved in various applications. This research reports the damage detection of a pressure box employed in space habitat that operates in harsh environment where both structural failure and bolt joint loosening may occur. These failure modes are extremely hard to model based on first principles. We explore proper sensing mechanism and the associated inverse analysis algorithm that can elucidate the health condition of the pressure box. It is identified that piezoelectric impedance based active interrogation can provide necessary information for damage detection in such a system. Concurrently, deep learning technique leveraging spatial convolutional neural network is synthesized to analyze the raw data acquired and identify different types of damage. By training the deep learning model on a dataset of healthy and various damage scenarios, we can achieve high accuracy in identifying the presence of damage and its type. This research provides a data-driven methodology for structural damage detection using deep learning and has the potential to be extended to various systems with different failure modes.

Combining Security and Reliability of Critical Infrastructures: The Concept of Securability

The digital revolution has made people more dependent on ICT technology to perform everyday tasks, whether at home or at work [...]

An Immersive Virtual Reality Training Game for Power Substations Evaluated in Terms of Usability and Engagement

Safety-focused training is essential for the operation and maintenance concentrated on the reliability of critical infrastructures, such as power grids. This paper introduces and evaluates a system for power substation operational training by exploring and interacting with realistic models in virtual worlds using serious games. The virtual reality (VR) simulator used building information modelling (BIM) from a 115 kV substation to develop a scenario with high technical detail suitable for professional training. This system created interactive models that could be explored using a first-person-perspective serious game in a cave automatic virtual environment (CAVE). Different operational missions could be carried out in the serious game, allowing several skills to be coached. The suitability for vocational training carried out by utility companies was evaluated in terms of usability and engagement. The evaluation used a System Usability Scale (SUS) and a Game Engagement Questionnaire (GEQ) filled by 16 power substation operators demonstrating marginally acceptable usability, with improvement opportunities and high acceptance (by utility technicians) of this system for operation training focused on safety in such hazardous tasks.

Targeting Reputation: A New Vector for Attacks to Critical Infrastructures

A substantial portion of critical information infrastructures in advanced economies comprises former public utilities, which in the 1980s/90s were fully or partially privatized, a change justified mainly on economic efficiency grounds. This entailed that these utility companies had to compete in the free market, thus being exposed to the same risks/opportunities as private companies. Much like businesses in other industrial sectors, utility companies have increasingly joined social media over the last decade, as &amp;lsquo;digital&amp;rsquo; visibility through social networking platforms, such as Facebook, Twitter, and Instagram has become fundamental. The new (privatized) utilities have relied on marketing and ad campaigns to promote their business and generate revenues. Trust and reputation for companies are primary resources to attract new customers and/or keep old ones, especially for companies with a wide customer base. Trust and reputation are difficult assets to preserve on social media, as they can be subject to negative attacks, including fake campaigns. This paper is a probe that explores a potential attack vector to critical infrastructures via weakening customer and investor trust in (the now private) utilities by blemishing CII-utilities&amp;rsquo; reputation on social media. More specifically, the paper considers the possibility of attacks that have the potential to undermine the stability and reliability of critical infrastructures and advances a preliminary justification of why that may happen. We do this by looking at cases in which negative social media campaigns with fake content have been successfully implemented via digital tools.

A Unified Approach for Reliability Assessment of Critical Infrastructures Using Graph Theory and Entropy

The growth in the critical infrastructure (CI) complexity, interdependency, and data flows has required enhanced reliability assessment methods. This paper proposes a reliability assessment model which considers various sources affecting the CI performance, including internal and external fault sources including natural disasters and malicious attacks. The paper proposes a hybrid data and model-based approach for a comprehensive CI reliability assessment, which considers fault source effects on CI subsystems and their interdependencies. The Shannon entropy concept is proposed for detecting any variations in the system information fault sources. The graph concept is used to determine interdependencies of CI subsystems. We have presented two case studies to illustrate the effectiveness of the proposed method, and compare the results with similar studies.

Reliability Approach to Resilience of Critical Infrastructure Impacted by Operation Process

Abstract The paper focuses on a critical infrastructure reliability and resilience to its operation process and particularly is devoted to critical infrastructure reliability and resilience indicators. First, the model of critical infrastructure reliability without considering outside impacts is proposed and applied to determine the port oil terminal reliability indicators. In the next step, the operation impact model of critical infrastructure reliability is created and applied to reliability and resilience analysis of the port oil terminal. The comparison of the port oil terminal critical infrastructure reliability indicators without considering outside impacts with indicators considering impact of its operation process is performed. A significant influence of the operation process on the port oil terminal reliability is proved.

Internet of Malicious Things: Correlating Active and Passive Measurements for Inferring and Characterizing Internet-Scale Unsolicited IoT Devices

Advancements computing, communication, and sensing technologies are making it possible to embed, control, and gather vital information from tiny devices that are being deployed and utilized practically every aspect of our modernized society. From smart home appliances to municipal water and electric industrial facilities to our everyday work environments, the next Internet frontier, dubbed IoT, is promising to revolutionize our lives and tackle some of our nations' most pressing challenges. While the seamless interconnection of IoT devices with the physical realm is envisioned to bring a plethora of critical improvements many aspects and diverse domains, it will undoubtedly pave the way for attackers that will target and exploit such devices, threatening the integrity of their data and the reliability of critical infrastructure. Further, such compromised devices will undeniably be leveraged as the next generation of botnets, given their increased processing capabilities and abundant bandwidth. While several demonstrations exist the literature describing the exploitation procedures of a number of IoT devices, the up-to-date inference, characterization, and analysis of unsolicited IoT devices that are currently deployed in the wild is still its infancy. In this article, we address this imperative task by leveraging active and passive measurements to report on unsolicited Internet-scale IoT devices. This work describes a first step toward exploring the utilization of passive measurements combination with the results of active measurements to shed light on the Internet-scale insecurities of the IoT paradigm. By correlating results of Internet-wide scanning with Internet background radiation traffic, we disclose close to 14,000 compromised IoT devices diverse sectors, including critical infrastructure and smart home appliances. To this end, we also analyze their generated traffic to create effective mitigation signatures that could be deployed local IoT realms. To support largescale empirical data analytics the context of IoT, we make available the inferred and extracted IoT malicious raw data through an authenticated front-end service. The outcomes of this work confirm the existence of such compromised devices on an Internet scale, while the generated inferences and insights are postulated to be employed for inferring other similarly compromised IoT devices, addition to contributing to IoT cyber security situational awareness.

Control-Theoretic Methods for Cyberphysical Security: Geometric Principles for Optimal Cross-Layer Resilient Control Systems

Cyberphysical systems integrate physical processes, computational resources, and communication capabilities. Cyberphysical systems have permeated modern society, becoming prevalent in many domains, including energy production, health care, and telecommunications. Examples of cyberphysical systems include sensor networks, industrial automation systems, and critical infrastructures such as transportation networks, power generation and distribution networks, water and gas distribution networks, and advanced manufacturing systems. The integration of cybertechnologies with physical processes increases system efficiencies and, at the same time, introduces vulnerabilities that undermine the reliability of critical infrastructures. As recently highlighted by the Maroochy water breach in March 2000 [1], multiple recent power blackouts in Brazil [2], the SQL Slammer worm attack on the Davis-Besse nuclear plant in January 2003 [3], the StuxNet computer worm in June 2010 [4], and various industrial security incidents [5], cyberphysical systems are prone to failures and attacks on their physical infrastructure and cyberattacks on their data management and communication layer [6], [7].

Optimizing Reliability of Critical Infrastructures with Application to Port Oil Piping Transportation System

A new approach to reliability optimization of multistate complex systems with dependent components at variable operation conditions called critical infrastructures is proposed. The reliability function of the critical infrastructure system is introduced and optimized. Further, the procedure of the critical infrastructure reliability optimization using the linear programming method is proposed and adopted to reliability optimization of an oil piping transportation system operating at maritime port.

Modeling Reliability of Critical Infrastructures with Application to Port Oil Transportation System

A new approach to reliability investigations of multistate complex systems with dependent components at variable operation conditions called critical infrastructures is proposed. The multi-state reliability function of the critical infrastructure system is defined and determined for an exemplary m out of l critical infrastructure. In the developed model, it is assumed that the system components have the multistate exponential reliability functions with dependent departures rates from the subsets of the reliability states. The model is adopted to reliability prediction of an oil piping transportation system operating at maritime port.

Integrated Model for Security and Protection of Critical Infrastructure

The creation of a matrix of minimum mandatory requirements for building a reliable system to counter potential terrorist threats is essential to increase the security and protection of critical infrastructure. In order to obtain an objective assessment of the success achieved in terms of necessary level of security and the extent to which is reached the reliability of critical infrastructure we should use a standardized approach and criteria. This is the purpose of this article, namely, to present the characteristics of a methodology for creation and the characteristics of an integrated, standardized model in this area, which can become the foundation of a comprehensive approach to the successful implementation of the so called L-strategy, i.e. protection of the individual and infrastructures against the threat of terrorism.

Designing Infrastructures: Dilemmas of Design and the Reliability of Critical Infrastructures

Critical infrastructures (CIs) are ‘balky systems’ of highly diverse, networked components with such high performance variations among them that there are few modal behaviors that characterize these infrastructures as whole systems. Consequently, they are under‐determined with respect to design principles and the control variables available to operators. These features raise significant reliability challenges in the management of CIs. In particular, formal design strategies increasingly applied to these infrastructures threaten to undermine the skills and cognitive capacities of control operators and middle‐level ‘reliability professionals’ to manage them reliably and safely. We investigate these challenges and propose new design principles to protect and enhance high reliability management in CIs.