Cyber Components Research Articles

An investigation of healthcare professionals’ perspectives on the role of technology in child sexual abuse

ABSTRACT Evidence suggests an increasing involvement of technology and online activities in child sexual abuse (CSA); however, empirical knowledge of the phenomenon is limited. This study investigated the role technology/online activities play in CSA, from the perspectives of healthcare professionals. Fifteen multidisciplinary healthcare professionals specialising in CSA assessment and therapy for children and adolescents completed a semi-structured interview. Results indicate technology and online activities can feature in the facilitation, perpetration, and aftermath of CSA in various ways, presenting unique challenges for victims, caregivers, and involved professionals. A range of complexities were noted in relation to CSA where there is a cyber component (e.g. permanence and reach of CSA images online), and implications for assessment and therapy at the child, parent/guardian, and clinician levels (e.g. tolerating uncertainty regarding images existing online). These findings are summarised in a diagrammatic framework, alongside priorities and prerequisites for addressing the problem of online/technology-assisted CSA.

A Comprehensive Survey on Enabling Techniques in Secure and Resilient Smart Grids

Smart grids are a cornerstone of the transition to a decentralised, low-carbon energy system, which offer significant benefits, including increased reliability, improved energy efficiency, and seamless integration of renewable energy sources. However, ensuring the security and resilience of smart grids is paramount. Cyber attacks, physical disruptions, and other unforeseen threats pose a significant risk to the stability and functionality of the grid. This paper identifies the research gaps and technical hurdles that hinder the development of a robust and secure smart grid infrastructure. This paper addresses the critical gaps in smart grid security research, outlining the technical challenges and promising avenues for exploration by both the industry and academia. A novel framework designed to enhance the reliability and security of smart grids was proposed against cyber attacks, considering the interconnectedness of the physical and cyber components. The paper further explores future research trends and identifies the key open issues in the ongoing effort to strengthen the security and resilience of smart grids.

Cyber Strategy in Practice

National cyber security strategies had already undergone a period of evolution before Russia’s 2022 invasion of Ukraine, ostensibly in preparation for just such a catalyser of major cyber conflagration. But the cyber war never came. Tom Johansmeyer, Gareth Mott and Jason R C Nurse analyse the national cyber security strategies (NCSS) of the US, Ukraine and Russia – as significant cyber domain stakeholders in the conflict – and how aligned these strategies were for the cyber component of the Ukrainian war. In addition, they consider areas of reflection for the future evolution of NCSS documents, particularly in light of the current state of play with cyber operations. ■

A Risk Assessment Framework for Cyber-Physical Security in Distribution Grids with Grid-Edge DERs

Integration of inverter-based distributed energy resources (DERs) is reshaping the landscape of distribution grids to fulfill the socioeconomic, environmental, and sustainability goals. Addressing the technological challenges of DER grid integration requires an adaptive communication layer for efficient DER management and control. This transition has given rise to a cyberphysical system (CPS) architecture within the distribution system, causing new vulnerabilities for cyberphysical attacks. To better address potential threats, this paper presents a comprehensive risk assessment framework for cyberphysical security in distribution grids with grid-edge DERs. The framework incorporates a detailed CPS model accounting for dynamic DER characteristics within the distribution grid. It identifies vulnerabilities in DER communication systems, models attack scenarios, and addresses communication latency crucial for inverter control timescales. Subsequently, the quantification of attack impacts employs an attack probability model including both the vulnerability and criticality of cyber components. The proposed risk assessment framework was validated through testing on the modified IEEE 13-node and 123-node test feeders.

Bridging the Cyber–Physical Divide: A Novel Approach for Quantifying and Visualising the Cyber Risk of Physical Assets

Critical infrastructures and their physical assets are under increasing threat of cyber-attacks as technological integration creates cyber–physical systems (CPSs). This has led to an urgent need to better understand which physical assets in these systems are most at risk, but this requires crossing the divide between cyber and physical risk assessments. However, existing cyber-security methods generally focus solely on the vulnerabilities and security of the cyber network and efforts to quantify the impacts of these cyber vulnerabilities on physical assets are generally limited to the consideration of individual attacks, rather than system-wide risk assessments. Similarly, risk assessments of physical infrastructure systems generally ignore potential impacts due to cyber-attacks. To overcome this cyber–physical divide in risk assessment, we introduce a novel approach for assessing risk across this divide. The proposed approach assesses the cyber risk of physical assets as a function of the vulnerabilities of their connected cyber components, and the contribution of cyber components to this risk. The approach is demonstrated with a case study of the C-Town water distribution system. The results indicate that the approach shows a modified prioritisation of risk compared to that obtained using conventional cyber or physical assessments, highlighting the importance of considering the connection between cyber and physical components in risk assessments of critical infrastructure and their physical assets.

Resiliency of manufacturing systems in the Industry 4.0 era – a systematic literature review

PurposeWith the emergence of the different Industry 4.0 technologies and the interconnectedness between the physical and cyber components within manufacturing systems, the manufacturing environment is becoming more susceptible to unexpected disruptions, and manufacturing systems need to be even more resilient than before. Hence, the purpose of this work is to explore how does incorporating Industry 4.0 into current manufacturing systems affects (positively or negatively) its resiliency.Design/methodology/approachA Systematic Literature Review (SLR) was performed with a focus on studying the manufacturing system’s resilience when applying Industry 4.0 technologies. The SLR is composed of four phases, which are (1) questions formulation, (2) determining an adequate search strategy, (3) publications filtering and (4) analysis and interpretation.FindingsFrom the SLR results’ analysis, four potential research opportunities are proposed related to conducting additional research within the research themes in this field, considering less studied Industry 4.0 technologies or more than one technology, investigating the impact of some technologies on manufacturing system’s resilience, exploring more avenues to incorporate resiliency to preserve the state of the system, and suggesting metrics to quantify the resilience of manufacturing systems.Originality/valueAlthough there are a number of publications discussing the resiliency of manufacturing systems, none fully investigated this topic when different Industry 4.0 technologies have been considered. In addition to determining the current research state-of-art in this relatively new research area and identifying potential future research opportunities, the main value of this work is in providing insights about this research area across three different perspectives/streams: (1) Industry 4.0 technologies, (2) resiliency and (3) manufacturing systems and their intersections.

Cost-aware defense for parallel server systems against reliability and security failures

Parallel server systems in transportation, manufacturing, and computing heavily rely on dynamic routing using connected cyber components for computation and communication. Yet, these components remain vulnerable to random malfunctions and malicious attacks, motivating the need for resilient dynamic routing that is both traffic-stabilizing and cost-efficient. In this paper, we consider a parallel server system with dynamic routing subject to reliability and stability failures. For the reliability setting, we consider an infinite-horizon Markov decision process where the system operator strategically activates a protection mechanism upon each job arrival based on traffic state observations. We prove that an optimal deterministic threshold protecting policy exists based on the dynamic programming recursion of the Hamilton–Jacobi–Bellman equation. For the security setting, we extend the model to an infinite-horizon stochastic game where the attacker strategically manipulates routing assignments. We show that both players follow a threshold strategy at every Markov perfect equilibrium. For both failure settings, we also analyze the stability of the traffic queues. Finally, we develop approximate dynamic programming algorithms to compute the optimal/equilibrium policies and present numerical examples/experiments for validation and illustration.

Power generation allocation of cyber–physical power systems from a defense–attack–defense perspective

Cyber–physical power systems have emerged as intelligent devices integrate into traditional power systems. These systems, featuring an integration of the physical and cyber components, are exposed to compounded risks from both realms. This paper explores the optimization of generation node allocation with a defense–attack–defense perspective to bolster the system’s resistance to external deliberate attacks. A trilevel optimization model is introduced, addressing the complications arising from communication line failures. This model encompasses upper-level defenders orchestrating generation allocation, middle-level attackers executing deliberate attacks, and lower-level operators optimizing system performance. The model leverages the Column-and-Constraint Generation (C&CG) approach to derive the optimal generation allocation strategy. Empirical validation using two IEEE test cases demonstrates the model’s effectiveness. Results highlight a significant escalation in total load loss upon communication line failures, and underscore the critical role of decentralized generation allocation in enhancing system resilience. These findings hold the potential to inform strategies for safeguarding cyber–physical power systems and reinforcing the architecture of resilient smart grids.

Flexible operation and maintenance optimization of aging cyber-physical energy systems by deep reinforcement learning

Cyber-Physical Energy Systems (CPESs) integrate cyber and hardware components to ensure a reliable and safe physical power production and supply. Renewable Energy Sources (RESs) add uncertainty to energy demand that can be dealt with flexible operation (e.g., load-following) of CPES; at the same time, scenarios that could result in severe consequences due to both component stochastic failures and aging of the cyber system of CPES (commonly overlooked) must be accounted for Operation & Maintenance (O&M) planning. In this paper, we make use of Deep Reinforcement Learning (DRL) to search for the optimal O&M strategy that, not only considers the actual system hardware components health conditions and their Remaining Useful Life (RUL), but also the possible accident scenarios caused by the failures and the aging of the hardware and the cyber components, respectively. The novelty of the work lies in embedding the cyber aging model into the CPES model of production planning and failure process; this model is used to help the RL agent, trained with Proximal Policy Optimization (PPO) and Imitation Learning (IL), finding the proper rejuvenation timing for the cyber system accounting for the uncertainty of the cyber system aging process. An application is provided, with regards to the Advanced Lead-cooled Fast Reactor European Demonstrator (ALFRED).

Dynamic Modeling of Nonlinear Systems in Cyber-Physical Environments

Complex behavior understanding and prediction relies on dynamic modeling of nonlinear systems in cyber-physical contexts. Critical for improving control systems, anticipating maintenance needs, and bolstering resilience, it permits accurate portrayal of complex interactions between cyber and physical components. Guaranteeing efficient and adaptable performance in networked ecosystems is made possible by this insight. Dynamic modeling of nonlinear systems (DM-NS) in cyber-physical settings has a number of challenges, such as the need to accurately depict complex interdependencies, accommodate changes in real-time, and reduce uncertainty. Advanced modeling techniques are required to capture the subtle behaviours and maintain the accuracy of the models while trying to balance the complicated interactions between physical and cyber components. A novel strategy is suggested to tackle these obstacles: Analysing Nonlinear Systems Cyber-Physical Modeling (ANSC-PM). By integrating complex mathematical models with adaptive learning algorithms, ANSC-PM is able to characterize nonlinear system behaviours, taking into account the dynamic interplay between cyber and physical components. To remain relevant as system dynamics change, the suggested method adjusts in real-time. Numerous disciplines find ANSC-PM useful, such as control system optimization, robotic predictive maintenance, and networked environment resilience enhancement. The effectiveness of ANSC-PM is determined by careful simulation analysis, which provides insight into its possible benefits in improving system performance, flexibility, and resilience in complex cyber-physical settings. Developing a thorough and flexible strategy that is adapted to the intricacies of cyber-physical systems, this research makes a substantial contribution to the progress of dynamic modeling approaches.

Actuator Anomaly Detection in Linear Parabolic Distributed Parameter Cyber-Physical Systems

Distributed parameter cyber-physical systems (DPCPSs) are characterized by two features: 1) they exhibit spatio-temporal (distributed parameter) dynamics and 2) there exist cyber components in the form of communication channels between the physical plant and control system. DPCPSs are vulnerable to anomalies such as nonmalicious physical faults and malicious cyber-attacks. Although the safety and security aspects of CPSs modeled by ordinary differential equations (ODEs) have been extensively explored during the past decade, security of DPCPSs has not received its due attention despite its safety-critical nature. In this work, we explore anomaly diagnostics in DPCPSs from a system theoretic viewpoint. Specifically, we focus on DPCPSs modeled by linear parabolic partial differential equations (PDEs) subject to physical faults and cyber-attacks in the actuation channel. First, we explore the detectability of malicious anomalies (i.e., cyber-attacks) and derive conditions for stealthy attacks in the context of output-feedback-based monitoring. Such stealthy attacks essentially illustrate the fundamental limitations of feedback-based monitoring algorithms given perfect adversarial knowledge. Subsequently, focusing on the anomalies that are detectable, we develop a design framework for anomaly detection algorithms based on output injection observers. Such detection algorithm explicitly considers the conflicting objectives of robustness to uncertainties and sensitivity to anomalies in their design. Finally, theoretical analysis and simulation studies are performed to illustrate the effectiveness of the proposed approach.

A Holistic Review of Cyber-Physical-Social Systems: New Directions and Opportunities.

A Cyber-Physical-Social System (CPSS) is an evolving subset of Cyber-Physical Systems (CPS), which involve the interlinking of the cyber, physical, and social domains within a system-of-systems mindset. CPSS is in a growing state, which combines secure digital technologies with physical systems (e.g., sensors and actuators) and incorporates social aspects (e.g., human interactions and behaviors, and societal norms) to facilitate automated and secure services to end-users and organisations. This paper reviews the field of CPSS, especially in the scope of complexity theory and cyber security to determine its impact on CPS and social media's influence activities. The significance of CPSS lies in its potential to provide solutions to complex societal problems that are difficult to address through traditional approaches. With the integration of physical, social, and cyber components, CPSS can realize the full potential of IoT, big data analytics, and machine learning, leading to increased efficiency, improved sustainability and better decision making. CPSS presents exciting opportunities for innovation and advancement in multiple domains, improving the quality of life for people around the world. Research challenges to CPSS include the integration of hard and soft system components within all three domains, in addition to sociological metrics, data security, processing optimization and ethical implications. The findings of this paper note key research trends in the fields of CPSS, and recent novel contributions, followed by identified research gaps and future work.

Cyber Restoration of Power Systems: Concept and Methodology for Resilient Observability

In order to have a properly functioning cyber–physical power system, the operational data need to be properly measured, transmitted, and processed. In case of a malicious event on the cyber layer of the power system, such as the wide-area monitoring system, cyber components, such as phasor measurement units (PMUs), communication routers, and phasor data concentrators (PDCs) may be compromised, leading to an unobservable power system. This article proposes the concept of cyber restoration of power systems, and an optimal restoration scheme to recover the system observability swiftly after massive interruptions. The cyber restoration problem is formulated as a mixed integer linear programming (MILP) problem considering PMU measurability, communication network connectivity, and PDC processability conditions, as well as cyber restoration resources as constraints. Results in the IEEE 57-bus system validate that the proposed optimization method can provide solutions that recover system observability much faster than heuristic methods, demonstrating the need for systematic cyber restoration planning research and implementation.

Defending the Defender: Adversarial Learning Based Defending Strategy for Learning Based Security Methods in Cyber-Physical Systems (CPS)

Cyber-Physical Systems (CPS) are prone to many security exploitations due to a greater attack surface being introduced by their cyber component by the nature of their remote accessibility or non-isolated capability. Security exploitations, on the other hand, rise in complexities, aiming for more powerful attacks and evasion from detections. The real-world applicability of CPS thus poses a question mark due to security infringements. Researchers have been developing new and robust techniques to enhance the security of these systems. Many techniques and security aspects are being considered to build robust security systems; these include attack prevention, attack detection, and attack mitigation as security development techniques with consideration of confidentiality, integrity, and availability as some of the important security aspects. In this paper, we have proposed machine learning-based intelligent attack detection strategies which have evolved as a result of failures in traditional signature-based techniques to detect zero-day attacks and attacks of a complex nature. Many researchers have evaluated the feasibility of learning models in the security domain and pointed out their capability to detect known as well as unknown attacks (zero-day attacks). However, these learning models are also vulnerable to adversarial attacks like poisoning attacks, evasion attacks, and exploration attacks. To make use of a robust-cum-intelligent security mechanism, we have proposed an adversarial learning-based defense strategy for the security of CPS to ensure CPS security and invoke resilience against adversarial attacks. We have evaluated the proposed strategy through the implementation of Random Forest (RF), Artificial Neural Network (ANN), and Long Short-Term Memory (LSTM) on the ToN_IoT Network dataset and an adversarial dataset generated through the Generative Adversarial Network (GAN) model.

Cyber Hacking Breaches Prediction using Machine Learning

Cyber-attacks are a major threat to these systems. Unlike faults that occur by accidents in cyber-physical systems, cyber-attacks occur intelligently and stealthily. Some of these attacks which are called deception attacks, inject false data from sensors or controllers, and also by compromising with some cyber components, corrupt data, or enter misinformation into the system. If the system is unaware of the existence of these attacks, it won’t be able to detect them, and performance may be disrupted or disabled altogether. The proposed method in this study is to use the structure of deep neural networks for the detection phase, which should inform the system of the existence of the attack in the initial moments of the attack. In the presented control method, after the attack detection phase with the use of a deep neural network, the control system uses the reputation algorithm to isolate the misbehaving agent. Experimental analysis shows us that deep learning algorithms can detect attacks with higher performance than usual methods and can make cyber security simpler, more proactive, less expensive, and far more effective

Detecting Engine Anomalies Using Batteries

The automotive industry is increasingly deploying software solutions to provide value-added features for vehicles, especially in the era of vehicle electrification and automation. However, the ever-increasing cyber components of vehicles (i.e., computation, communication, and control) incur new risks of anomalies, as evident by the millions of vehicle recalls by different automakers. To mitigate these risks, we design the <monospace>B-Diag</monospace> , a battery-based diagnostic system detects engine anomalies with a cyber-physical approach. The core idea of <monospace>B-Diag</monospace> is to diagnose engines using the physically-induced correlations between battery voltage and engine variables, which is captured as a customized 3-layer correlation graph and a set of data-driven norm models describing the edges thereof. The design of <monospace>B-Diag</monospace> is steered by a dataset collected with a prototype system when driving a 2018 Subaru Crosstrek in real-life for over three months. Our evaluation shows <monospace>B-Diag</monospace> to detect anomalies of 17 engine variables with a <inline-formula><tex-math notation="LaTeX">$&gt;$</tex-math></inline-formula> <inline-formula><tex-math notation="LaTeX">$86\%$</tex-math></inline-formula> (up to <inline-formula><tex-math notation="LaTeX">$100\%$</tex-math></inline-formula> ) accuracy.

Digital twins in cyber effects modelling of IoT/CPS points of low resilience

The exponential increase of data volume and velocity have necessitated a tighter linkage of physical and cyber components in modern Cyber–physical systems (CPS) to achieve faster response times and autonomous component reconfiguration. To attain this degree of efficiency, the integration of virtual and physical components reinforced by artificial intelligence also promises to improve the resilience of these systems against organised and often skillful adversaries. The ability to visualise, validate, and illustrate the benefits of this integration, while taking into account improvements in cyber modelling and simulation tools and procedures, is critical to that adoption. Using Cyber Modelling and Simulation (M&S) this study evaluates the scale and complexity required to achieve an acceptable level of cyber resilience testing in an IoT-enabled critical national infrastructure (CNI). This research focuses on the benefits and challenges of integrating cyber modelling and simulation (M&S) with digital twins and threat source characterisation methodologies towards a cost-effective security and resilience assessment. Using our dedicated DT environment, we show how adversaries can utilise cyber–physical systems as a point of entry to a broader network in a scenario where they are trying to attack a port.

Cyber attack detection in healthcare data using cyber-physical system with optimized algorithm

A medical cyber-physical system (MCPS) integrates medical sensor devices with cyber (information) components, which creates a sensitive approach and provides security. The MCPS plays a vital role in hospitals by detecting attacks and protecting patients' medical information. Many research projects have been carried out to detect attacks on the generation of medical information in MCPS. The issues with existing algorithms are their inefficiency and time-consuming maximization of error rates. To overcome the challenges, this paper proposes detecting the attack using the fuzzy C-Means algorithm with artificial bee colony optimization (FCM-ABC). The novelty of the work is flexibility in deciding whether data points belong to actual users or attackers using the fuzzy c means degree [0,1] measuring technique of cluster formation. ABC is used in self-organizing the clusters with ABC collective intelligence. It monitors health information using sensor networks. The MCPS model interconnects the sensor devices remotely and collects the information. SVM has an accuracy rate of 76.32%, FCM has an accuracy rate of 81.34%, LSTM has an accuracy rate of 86.22%, and our proposed work, FCM-ABC, has an accuracy rate of 93.34%.

Formalising Autonomous Construction Sites with the Help of Abstract Mathematics

With the rapid development of modern technologies, autonomous or robotic construction sites are becoming a new reality in civil engineering. Despite various potential benefits of the automation of construction sites, there is still a lack of understanding of their complex nature combining physical and cyber components in one system. A typical approach to describing complex system structures is to use tools of abstract mathematics, which provide a high level of abstraction, allowing a formal description of the entire system while omitting non-essential details. Therefore, in this paper, autonomous construction is formalised using categorical ontology logs enhanced by abstract definitions of individual components of an autonomous construction system. In this context, followed by a brief introduction to category theory and ologs, exemplary algebraic definitions are given as a basis for the olog-based conceptual modelling of autonomous construction systems. As a result, any automated construction system can be described without providing exhausting detailed definitions of the system components. Existing ologs can be extended, contracted or revised to fit the given system or situation. To illustrate the descriptive capacity of ologs, a lattice of representations is presented. The main advantage of using the conceptual modelling approach presented in this paper is that any given real-world or engineering problem could be modelled with a mathematically sound background.

Ensure: Towards Reliable Control of Cyber-Physical Systems Under Uncertainty

Cyber-physical systems (CPSs) are complex ensembles of physical and cyber components that cooperate to offer dynamic and adaptive functionalities. Uncertainty can arise from a plethora of sources in the entangled components, ranging from the unreliable perception, the nondeterministic action effects, to even the changes in the environment. Existing controlling approaches, such as those using Markov decision process, have limited ability in handling uncertainty. To address the challenge, in this article, we novelly propose using partially observable Markov decision processes (POMDPs) to model CPS under uncertainty and show that common types of uncertainties can be modeled by partial observations and nondeterministic actions over probabilistic distributions. With POMDPs, strategies that can optimally control CPS are synthesized. We further propose a strategywise verification method, which resolves the difficult problem of verifying the entire POMDP, to offer reliable controlling strategies. Experiments on two representative cases of CPS show promising results compared with existing approaches.