Cyber-physical Infrastructure Research Articles

A dynamic importance function for accidental scenarios generation by RESTART in the computational risk assessment of cyber-physical infrastructures

The Computational Risk Assessment (CRA) of Cyber-Physical Systems (CPSs) calls for the analysis of accidental scenarios emerging from the complexities and interdependencies typical of CPSs. Generating these scenarios via crude Monte Carlo Simulation (MCS) is impractical due to the high computational demand of simulation codes of CPSs, considering the combinatorial number of possible scenarios. In this paper, we tailor the use of Repetitive Simulation Trials After Reaching Thresholds (RESTART), a rare-event simulation method of literature, to efficiently generate relevant accidental scenarios. The tailored RESTART is guided by a dynamic Importance Function (IF) originally introduced here to dynamically characterize the relevance of the scenarios with reference to the current topology of the CPS and the susceptibility of its components. Two case studies of increasing complexity are considered: a single power grid and a CPS consisting of an Integrated Power and Telecommunication (IP&TLC) infrastructure. Results show that RESTART mines out more relevant scenarios than crude MCS for a number of different IFs based on vulnerability metrics of literature, and thus particularly efficiently when the novel IF is adopted.

Resiliency of forecasting methods in different application areas of smart grids: A review and future prospects

The cyber–physical infrastructure of a smart grid requires data-dependent artificial intelligence (AI)-based forecasting schemes for predicting different aspects for the short- to long-term, where AI-based schemes include machine learning (ML), deep learning (DL), and hybrid models. These forecasting schemes in different application areas of a smart grid can be vulnerable to cyber-attacks, which is yet to be addressed from a broad perspective. This work reviews the literature addressing the vulnerability of forecasting schemes in smart grids with a categorization of application areas. The existing research works addressing cyber-security or cyber resiliency are reviewed and then presented in an organized manner according to application areas to highlight their advantages and disadvantages. The findings of this review indicate a critical need to develop accurate and robust AI-based forecasting schemes capable of withstanding diverse attack scenarios in each sector, while addressing unsymmetrical attention to different sectors of smart grids. Hence, this review provides a comprehensive overview of the current literature and emphasizes the necessity for the research community to advance toward developing attack-resilient AI-based forecasting schemes designed explicitly for smart grids.

A systematic review of optimization methods for recovery planning in cyber–physical infrastructure networks: Current state and future trends

Cyber–Physical Systems are increasingly complex and frequently integrated into modern societies via critical infrastructure systems, products, and services. Consequently, there is a need for reliable functionality of these complex systems under various scenarios, from physical failures due to aging to cyber attacks. However, the development of effective strategies to restore disrupted cyber–physical infrastructure systems continues to be a major challenge. Even though there have been an increasing number of papers evaluating recovery planning in cyber–physical infrastructures networks, a comprehensive literature review focusing on mathematical modeling and optimization methods is still lacking. Thus, this study critically analyzes the literature on optimization techniques for recovery planning of cyber–physical infrastructure networks after a disruption, to synthesize key findings on the current methods in this domain. A total of 152 relevant research papers are reviewed following an extensive assessment of all major scientific databases. The main mathematical modeling practices and optimization methods are identified for both deterministic and stochastic formulations, categorizing them based on the solution approach (exact, heuristic, metaheuristic), objective function, and network size. Having identified the gaps, a set of future trends for both the methodology and application of optimization algorithms is presented. Overall, there is a need to shift toward scalable optimization solution algorithms, empowered by data-driven methods and machine learning algorithms, to provide reliable and computationally efficient decision-support systems for decision-makers and practitioners.

Predictive Maintenance with Linguistic Text Mining

The escalating intricacy of industrial systems necessitates strategies for augmenting the reliability and efficiency of industrial machinery to curtail downtime. In such a context, predictive maintenance (PdM) has surfaced as a pivotal strategy. The amalgamation of cyber-physical systems, IoT devices, and real-time data analytics, emblematic of Industry 4.0, proffers novel avenues to refine maintenance of production equipment from both technical and managerial standpoints, serving as a supportive technology to enhance the precision and efficacy of predictive maintenance. This paper presents an innovative approach that melds text mining techniques with the cyber-physical infrastructure of a manufacturing sector. The aim is to improve the precision and promptness of predictive maintenance within industrial settings. The text mining framework is designed to sift through extensive log files containing data on the status of operational parameters. These datasets encompass information generated by sensors or computed by the control system throughout the production process execution. The algorithm aids in forecasting potential equipment failures, thereby curtailing maintenance costs and fortifying overall system resilience. Furthermore, we substantiate the efficacy of our approach through a case study involving a real-world industrial machine. This research contributes to the progression of predictive maintenance strategies by leveraging the wealth of textual information available within industrial environments, ultimately bolstering equipment reliability and operational efficiency.

Digital twin-assisted intelligent anomaly detection system for Internet of Things

The proliferation of Internet of Things (IoT) devices within the cyber–physical infrastructure has raised privacy and security concerns. This is because processing large amounts of data and extracting anomalous data cannot be imprecisely identified with the limited storage and processing capabilities of IoT devices. Against this backdrop, edge computing is a networking paradigm to reshape the next generation of IoT-based applications, which brings computation and data storage closer to the data sources. However, since edge servers are difficult to obtain security protections like in centralized data centers, this makes them more vulnerable to security attacks. To address this issue, we propose a novel digital twin system architecture in edge computing-based Internet of Things (IoT) networks, which allows efficient data processing for anomaly detection at the network edge. Digital twin is a replica of the real world in a virtual environment with continuous information exchange. Our digital twin-assisted solution gives a data-driven strategy against the cell outage and simulates the behavior and patterns of the physical assets in the digital world. Powered by data processing capabilities at the network edge, the proposed digital twin-assisted model can help extract features and behaviors, reduce noise from data generated in the network physical model, and filter out redundant data for a better characterization of the network physical model in the anomaly detection service. To this extent, the contributions in this paper are threefold: We first propose a novel digital twin-assisted anomaly detection framework that processes high-dimensional data efficiently and improves the system performance despite the imbalanced classes. Second, we define an autoencoder-based threshold algorithm and evaluate the eXtreme Gradient Boosting (XGBoost) classifier to implement self-learning capability into the digital twin for an accurate prediction. Third, we extract the features, dependencies, and relationships to apply the learned model to the new and unseen dataset for real-time monitoring and optimization and evaluate the performance of the proposed model with both real-time and historical data. Finally, we analyze the performance of different classifiers to validate our model. We use four different datasets and demonstrate that our solution outperforms the state-of-the-art techniques in terms of accuracy performance, AUC score, and ROC curve.

Distributed stochastic energy coordination for residential prosumers: Framework and implementation

This paper addresses the problem of energy coordination among customers in a local energy market to meet heating needs. Accordingly, this paper proposes a demand response framework for the energy coordination of multiple residential prosumers in a hierarchical coordination game-theoretic scenario. That consists of a benevolent coordinator and multiple residential prosumers integrated with solar PV under demand uncertainties as a consequence of forecasting errors in weather data and non-shiftable load. The coordinator and customers are considered to be a leader and various followers, respectively. The coordinator employs a cost-shared strategy to reduce peak demand. Customers maximize individual welfare by employing stochastic programming through demand flexibility realized via electric baseboard heaters and electric thermal storage. Subsequently, the proposed strategy is implemented through a cyber–physical infrastructure incorporating a distributed computing platform with embedded systems and extensive simulations using accurate weather and real-life energy consumption data. A comparative analysis is performed using a deterministic baseline with perfect information, i.e. no forecasting errors. In addition, the proposed mechanism is tested on an adapted CIGRE low-voltage benchmark system. Simulation results demonstrate the techno-economical feasibility of the proposed solution in uncertain environments. Furthermore, they recommend practical strategies for short-term planning of grids highly penetrated by renewables and intelligent devices by increasing their flexibility and leveraging salient features of cyber–physical systems.

Cyber–physical system architecture of autonomous robot ecosystem for industrial asset monitoring

Driven by advancements in Industry 4.0, the Internet of Things (IoT), digital twins (DT), and cyber–physical systems (CPS), there is a growing interest in the digitalizing of asset integrity management. CPS, in particular, is a pivotal technology for the development of intelligent and interconnected systems. The design of a scalable, low-latency communication network with efficient data management is crucial for connecting physical and digital twins in heterogeneous robot fleets. This paper introduces a generalized cyber–physical architecture aimed at governing an autonomous multi-robot ecosystem via a scalable communication network. The objective is to ensure accurate and near-real-time perception of the remote environment by digital twins during robot missions. Our approach integrates techniques such as downsampling, compression, and dynamic bandwidth management to facilitate effective communication and cooperative inspection missions. This allow for efficient bi-directional data exchange between digital and physical twins, thereby enhancing the overall performance of the system.This study contributes to the ongoing research on the deployment of cyber–physical systems for heterogeneous multi-robot fleets in remote inspection missions. The feasibility of the approach has been demonstrated through simulations in a representative environment. In these experiments, a fleet of robots is used to map an unknown building and generate a common 3D probabilistic voxel-grid map, while evaluating and managing bandwidth requirements. This study represents a step forward towards the practical implementation of continuous remote inspection with multi-robot systems through cyber–physical infrastructure. It offers potential improvements in scalability, interoperability, and performance for industrial asset monitoring.

Heterogeneous cyber-physical network coexistence through interference contribution rate and uplink power control algorithm (ICR-UPCA) in 6G edge cells

Optimizing power control for interference mitigation at the network cell edge is pivotal in enhancing capacity within a heterogeneous cyber-physical infrastructure, such as smart cities, manufacturing, healthcare, energy grids, transportation, and agriculture, among others. In this paper, we consider the intricate dynamics of Internet of Things (IoT) 5/6G edge users, with a particular focus on the Interference Contribution Rate (ICR), where macro and femtocells are critical network infrastructures. Existing approaches has drawbacks such as computational complexity, overhead, and co-channel interference, among others. However, to fully address interference challenges from the coexistence of diverse network hierarchies, preserving the Quality of Service for femtocell users is prioritized. The paper concurrently enhances the handoff mechanism of cell edge users in the macro cell network. A two-tier heterogeneous network (HetNet) is utilized to initially assess the contribution of edge user equipment (UE) to interference levels during its active state while quantifying it as ICR. Game theory is used to formulate a cohesive model for the coexistence of macro cell (MUE) and femtocell users (FUE). ICR-based uplink power control and reference signal received quality (RSRQ)-based handoff algorithms are deployed to regulate interference levels and enhance the Signal-to-Interference-Noise Ratio (SINR) of the MUE at the cell edge. This is achieved through coordinated transmit power adjustments by both user types. Results indicate a 6.67 % channel capacity loss (interference tolerance) by the FUE, leading to a 12.5 % improvement, translating to approximately 4 Mbps and 1 Mbps channel enhancements, respectively. The MUE and FUE can effectively coordinate power control with minimal overhead, accepting compromises in network channel quality. This approach facilitates improved MUE data access rates while ensuring the preservation of FUE. We show that interference is successfully mitigated through power control in heterogeneous networks with lower computational complexity.

Continuity planning for public health crises: Designing workplace redundancies for organizational resilience.

Continuity planning prepares organizations to maintain essential functions despite disruptions to critical infrastructure that occur during crises. Continuity planning is especially important for Public-Safety Answering Points (PSAPs), which must prepare to answer 911 calls and dispatch first responders in all-hazard environments, including public health crises such as the COVID-19 pandemic. However, continuity planning typically focuses on disruptions to cyber-physical infrastructure rather than social infrastructure disruptions that occur when outbreaks of communicable disease limit the ability of essential personnel to perform an organization's essential functions. Reporting findings from interviews with US officials, this study examines how PSAPs decentralized essential personnel by designing redundant workplaces during the COVID-19 pandemic. Realizing existing continuity plans prepared PSAPs to relocate and recentralize essential personnel in a single, shared workplace, officials developed new plans to protect and decentralize telecommunicators across multiple, separate workplaces. To do so, PSAPs achieved passive, standby, and active workplace redundancies that recommend continuity planning objectives and requirements for organizations preparing for future public health crises.

Trilattice-Based Access Control Models: How to Secure Current Computer Network Mikhail

Designing security, from the hardware level, is essential to ensure the integrity of the intelligent cyberphysical infrastructure that is the Industrial Internet of Things (IIoT). If intelligent cyber-physical infrastructure fails to do the right things because it is insecure and vulnerable, then there will be negative social consequences [1]. Security is, in a sense, the access control to IIoT systems, which increasingly relies on the ability to compose different policies. Therefore, the advantage in any framework for compiling policies is that it is intuitive, formal, expressive, application-independent, as well as expandable to create domain-specific instances. Recently, such a scheme was proposed based on Belnap logic FOUR2 [2]. Four values of the Belnap bilattice have been interpreted as grant, deny, conflict, or unspecified with respect to access-control policy. Belnap's four-valued logic has found a variety of applications in various fields, such as deductive database theory, distributed logic programming, and other areas. However, it turns out that the truth order in FOUR2 is a truth-and-falsity order at the same time [3]. The smallest lattice, where the orders of truth and falsity are independent of each other, which is especially important for security policy, is that of Shramko-Wansing’s SIXTEEN3. This generalization is well-motivated and leads from the bilattice FOUR2 with an information and a truth-and-falsity ordering to another algebraic structure, namely the trilattice SIXTEEN3 with an information ordering together with a truth ordering and a (distinct) falsity ordering. Based on SIXTEEN3 and new Boolean predicates to control access [4], we define an expressive access-control policy language, having composition statements based on the statements of Schramko-Wansing’s logic. Natural orderings on politics are obtained by independent lifting the orders of truth and falsity of trilattice, which results in a query language in which conflict freedom analysis can be developed. The reduction of formal verification of queries to that on predicates over access requests enables to carry out policy analysis. We evaluate our approach through examples of control access model policy.

Cyber-Physical System Security Based on Human Activity Recognition through IoT Cloud Computing

Cyber-physical security is vital for protecting key computing infrastructure against cyber attacks. Individuals, corporations, and society can all suffer considerable digital asset losses due to cyber attacks, including data loss, theft, financial loss, reputation harm, company interruption, infrastructure damage, ransomware attacks, and espionage. A cyber-physical attack harms both digital and physical assets. Cyber-physical system security is more challenging than software-level cyber security because it requires physical inspection and monitoring. This paper proposes an innovative and effective algorithm to strengthen cyber-physical security (CPS) with minimal human intervention. It is an approach based on human activity recognition (HAR), where GoogleNet–BiLSTM network hybridization has been used to recognize suspicious activities in the cyber-physical infrastructure perimeter. The proposed HAR-CPS algorithm classifies suspicious activities from real-time video surveillance with an average accuracy of 73.15%. It incorporates machine vision at the IoT edge (Mez) technology to make the system latency tolerant. Dual-layer security has been ensured by operating the proposed algorithm and the GoogleNet–BiLSTM hybrid network from a cloud server, which ensures the security of the proposed security system. The innovative optimization scheme makes it possible to strengthen cyber-physical security at only USD 4.29±0.29 per month.

Empirical Analysis of Privacy Preservation Models for Cyber Physical Deployments from a Pragmatic Perspective

The difficulty of privacy protection in cyber-physical installations encompasses several sectors and calls for methods like encryption, hashing, secure routing, obfuscation, and data exchange, among others. To create a privacy preservation model for cyber physical deployments, it is advised that data privacy, location privacy, temporal privacy, node privacy, route privacy, and other types of privacy be taken into account. Consideration must also be given to other types of privacy, such as temporal privacy. The computationally challenging process of incorporating these models into any wireless network also affects quality of service (QoS) variables including end-to-end latency, throughput, energy use, and packet delivery ratio. The best privacy models must be used by network designers and should have the least negative influence on these quality-of-service characteristics. The designers used common privacy models for the goal of protecting cyber-physical infrastructure in order to achieve this. The limitations of these installations' interconnection and interface-ability are not taken into account in this. As a result, even while network security has increased, the network's overall quality of service has dropped. The many state-of-the-art methods for preserving privacy in cyber-physical deployments without compromising their performance in terms of quality of service are examined and analyzed in this research. Lowering the likelihood that such circumstances might arise is the aim of this investigation and review. These models are rated according to how much privacy they provide, how long it takes from start to finish to transfer data, how much energy they use, and how fast their networks are. In order to maximize privacy while maintaining a high degree of service performance, the comparison will assist network designers and researchers in selecting the optimal models for their particular deployments. Additionally, the author of this book offers a variety of tactics that, when used together, might improve each reader's performance. This study also provides a range of tried-and-true machine learning approaches that networks may take into account and examine in order to enhance their privacy performance.

Accurate and fast machine learning algorithm for systems outage prediction

Cyber-attacks (CAs) on electrical networks in presents of renewable energies, particularly solar energy, have become more complex and sophisticated over the past few years by making severe system outages. In light of increased automation in solar-based energy industries, a holistic cyber-physical infrastructure must be considered to predict the effect of CAs on electrical networks and the ways to enhance its resilience. The present study examines the resilience characteristics at the equipment area of the diverse control methods and their effect on the outage severity of the smart grid using digital twins (DT) simulation technology. The paper presents a machine learning based metric for measuring the resilience of cyber-physical features that considers device-level characteristics, vulnerabilities, and system models. Resiliency refers to a system's capability of providing energy even during severe contingencies and relates to the ability to resist, forecast, and recover. An example based on the newest CA against Ukraine has been provided and simulated on DT environment. A case study is proposed to illustrate how cyber-physical resilience metrics can be applied to improve operators' situational awareness and provide more proactive or corrective control measures for improving resilience.

Analyzing the tradeoff between vulnerability and recoverability investments for interdependent infrastructure networks

Critical cyber-physical infrastructure networks such as electric power and water networks form the backbone of a functional society as these networks provide the main resources for economic productivity, healthy communities, and general day-to-day operations. However, (i) these infrastructure networks have become more interdependent on each other in terms of functionality, and (ii) community networks become more dependent on these critical infrastructures in terms of existence. Thus, studying the entirety of their resilience process—both vulnerability and recoverability against a disruption—is of importance, particularly analyzing how investing (i) before to strengthen components versus (ii) afterward to restore disrupted components can affect the resilience of infrastructure networks in terms of system performance and time. This paper takes a first step toward analyzing the tradeoff between reducing vulnerability and enhancing recoverability in interdependent infrastructure networks with multiple time and budget allocations, accounting for not only the performance of the physical networks but also the potentially socially vulnerable communities that rely upon them. The proposed model (i) maximizes a measure of the resilience of the interdependent infrastructure networks, while it (ii) minimizes the total cost associated with the pre- and post-event resource allocation. The model is illustrated with networks in Shelby County, TN, USA.

Artificial intelligence underpins urban water infrastructure of the future: A holistic perspective

Abstract The potential of artificial intelligence (AI) in water management is widely recognised by research and practice communities alike, with an increasing number of applications showed tackling water supply, stormwater and wastewater management challenges. However, there is a critical knowledge gap in understanding the fundamental role of AI in the development of urban water infrastructure (UWI). This review aimed to provide an analysis of how AI could be aligned to support the future development of UWI systems. Four types of AI analytics – descriptive, diagnostic, predictive and prescriptive – are discussed and linked to the improvement in the performance of UWI systems from three categories: reliability, resilience and sustainability. It is envisioned that AI technology will play a pivotal role in UWI transitioning to the future through underpinning the five development pathways: decentralisation, circular economy, greening, decarbonisation and automation. The barriers in improving AI adoption in the real world are also highlighted from four dimensions: cyber-physical infrastructure, institutional governance, social-economic systems and technological development in wider society. Embedding AI in the development pathways and tackling the barriers can ensure that AI-empowered systems are deployed in an equitable and responsible way to improve the resilience and sustainability of future UWI systems.

Multi-Access Edge Computing assisted ultra-low energy scheduling and harvesting in multi-hop Wireless Sensor and Actuator Network for energy neutral self-sustainable Next-gen Cyber-Physical System

Energy Neutrality is the need of the hour for future Cyber-physical infrastructure. Many past research works suggest low duty cycle networking for energy-efficient wireless sensor network; however, research problem still persist as these proposals have some practical deployment bottlenecks like sleep-latency and may trade-off with data-fidelity and Quality-of-Service (QoS) parameters. The research objective of this work is to propose a novel design for wireless sensor–actuator mote system to minimize energy consumption and compensate with harvesting to achieve energy neutral operation in multi-hop Wireless Sensor and Actuator Network (WSAN); A network scenario that is much needed for Internet of Thing (IoT) applications in future ready Next-gen Cyber Physical System (NG-CPS). In this paper, we have proposed a novel state-of-the-art Multi-Access/Mobile Edge Computing (MEC) assisted Low-Duty-Cycle Scheduling of Trans-Receiver for Store-Process-Then-Forward (LDCS-TR-SPTF) scheme in multi-hop WSAN for reducing the overall energy consumption. In addition to energy conservation, we have also proposed a hybrid harvesting to compensate energy to achieve energy neutral self-sustainable NG-CPS. The proposed novel LDCS-TR-SPTF has been implemented on multiple custom-made sensor–actuator motes equipped with ARM-based System-on-Chip (SoC) and IEEE 802.11 network interface. These motes are working both as end-nodes and relay nodes in a multi-hop WSAN testbed scenario for testing and validation of our proposed scheme. The results are promising with an overall energy conservation of 49.1% using our novel LDCS-TR-SPTF in comparison to stock IEEE 802.11. We have implemented solar energy harvester on wireless sensor–actuator motes to compensate the reduced energy consumption and achieved complete energy-neutrality. Motes are completely energy-autonomous in multi-hop WSAN scenario by eliminating the need of access to external power-grid for future cyber–physical system.

Conflict Detection and Resolution in IoT Systems: A Survey

Internet of Things (IoT) systems are becoming ubiquitous in various cyber–physical infrastructures, including buildings, vehicular traffic, goods transport and delivery, manufacturing, health care, urban farming, etc. Often multiple such IoT subsystems are deployed in the same physical area and designed, deployed, maintained, and perhaps even operated by different vendors or organizations (or “parties”). The collective operational behavior of multiple IoT subsystems can be characterized via (1) a set of operational rules and required safety properties and (2) a collection of IoT-based services or applications that interact with one another and share concurrent access to the devices. In both cases, this collective behavior often leads to situations where their operation may conflict, and the conflict resolution becomes complex due to lack of visibility into or understanding of the cross-subsystem interactions and inability to do cross-subsystem actuations. This article addresses the fundamental problem of detecting and resolving safety property violations. We detail the inherent complexities of the problem, survey the work already performed, and layout the future challenges. We also highlight the significance of detecting/resolving conflicts proactively, i.e., dynamically but with a look-ahead into the future based on the context.

Improving Quality of Experience Using Fuzzy Controller for Smart Homes

Internet of things is providing us numerous ways to improve our quality of experience by using smart cyber-physical infrastructure systems. Also, due to arrival of LED lighting systems, there is the possibility to improve user’s visual comfort at less cost. In our proposed model, by using a fuzzy inference system, used in cyber-physical infrastructure system, we save energy from the heating, ventilation and air conditioning system. This saved energy is used to improve the visual comfort of the user. Simulation results show that considering the visual comfort standard of 500 lux instead of 250 lux results in energy savings and ensures visual comfort. Together with the preservation of thermal comfort increases the overall users’ comfort. Since research confirms that users’ improved comfort results in up to 14% of increased productivity. Our model is unique in the sense that using fuzzy logic, indirectly improved the users’ productivity. By using our fuzzy logic controller on electric equipment, we can achieve improved users’ performance without paying any extra cost.

Modelling and Control of Complex Cyber-Physical Ecosystems

In this paper, we set up a mathematical framework for the modelling and control of complex cyber-physical ecosystems. In our setting, cyber-physical ecosystems (CPES) are cyber-physical systems of systems that are highly connected. CPES are understood as open and adaptive cyber-physical infrastructures. These networked systems combine cyber-physical systems with an interaction mechanism with other systems and the environment (ecosystem capability). The main focus will be on modelling cyber and physical interfaces that play an important role on the control of the emergent properties like safety and security.

Guest Editors’ Introduction to the Joint Special Section on Secure and Emerging Collaborative Computing and Intelligent Systems

The papers in this special section focus on secure and emerging collaborative computing and intelligent systems. The Internet, coupled with recent advances in computing and information technologies, such as IoT, mobile edge/ cloud computing, cyber-physical-social systems, and artificial intelligence/machine learning/deep learning, have paved the way for creating next-generation smart and intelligent systems and applications that can have transformative impact in our society while accelerating rapid scientific discoveries and innovations. Unprecedented cyber-social and cyber-physical infrastructures and systems that span geographic boundaries are possible because of the Internet and the growing number of collaboration-enabling technologies. With newer technologies and paradigms getting increasingly embedded in the computing platforms and networked information systems/ infrastructures that form the digital foundation for our personal, organizational, and social processes and activities, it is increasingly becoming critical that the trust, privacy, and security issues in such digital environments are holistically addressed to ensure the safety and well-being of individuals as well as our society.