Network Assets Research Articles

Generating Payloads of Power Monitoring Systems Compliant with Power Network Protocols Using Generative Adversarial Networks

In the network environment of power systems, payload generation is used to construct data packets, which are used to obtain data for the security management of network assets. Payloads generated by existing methods cannot satisfy the specifications of the protocols in power systems, resulting in low efficiency and information errors. In this paper, a payload generation model, LoadGAN, is proposed by using generative adversarial networks (GANs). Firstly, we find segmentation points to cut payloads into different segment sequences using sliding window schema based on Bayesian optimization. Then, we use different payload segments to train several child generators to generate corresponding parts of a whole payload. Segment sequences generated by these generators are assembled to form a whole new payload that is compliant with the specifications of the original network protocol. Experiments on the Mozi botnet dataset show that LoadGAN achieves precise payload segmentation while maintaining a high payload effectiveness of 85.5%, which is a 40% improvement compared to existing methods.

Mapping the Path of Digital Transformation: The Role of Leadership in Building Adaptive Organizational Culture

The development movement continues towards the digital transformation era, marked by rapid advances in information and computer technology (ICT) and the emergence of social media platforms that change the order of life to be more practical and accessible and deny the geographical distance between people. These changes require universities to adapt to the demands of rapid development. This study aims to explore the strategies carried out by university leaders in facing the challenges of the digital era and map the extent to which the transformation of university organizational culture is operated in the face of digital technology pressure. The research used a qualitative approach with a multi-site format in three private universities. Data analysis applies constant comparative analysis, which ends with a simultaneous analysis step. The results showed that university leaders are ready to implement an open strategy in facing the challenges of the digital era by utilizing these technological advances. However, there are differences in the utilization of network assets owned by institutions and the knowledge and experience of leaders. Organizational culture transformation is operated to the extent that institutions can master digital technology to optimize the performance of institutional services. Universities' engagement with digital technology does not disrupt the resilience of their organizational culture because it only leads to an adaptive surface layer.

Graph-Theory Metrics for the Prioritization of Water Distribution Network Assets of Tyndall AFB, Florida

Graph-Theory Metrics for the Prioritization of Water Distribution Network Assets of Tyndall AFB, Florida

Probabilistic pre-estimation of life-cycle costs of road bridges using dynamic Bayesian networks

Proper life-cycle cost (LCC) estimation of vital transportation network assets, such as road bridges, is crucial for cost-efficient projects. This study presents a probabilistic framework for the LCC estimation of concrete bridges based on Dynamic Bayesian Network (DBN) modelling that stakeholders can consult in projects’ preliminary stages for suitable decision-making. The proposed model consists of two separate modules. The first accounts for the probabilistic estimation of material quantities and their relative costs during the construction phase, specifically for the bridge’s superstructure and foundation. The second one refers to the operational phase of the bridge’s components. For illustrative purposes, the maintenance costs of deck expansion joints are presented, as they are regarded as vulnerable bridge components with regular repairs. The suggested DBN includes time-dependent and continuous variables thoroughly documented over multiple time stages. The DBN is formulated using actual records of 78 concrete bridges’ construction, deterioration, inspections and maintenance from the Egnatia Odos motorway in Greece. The presented approach is an effective tool for bridge management enhancement owing to stochastic dynamic interdependencies among parameters and continuous information updating.

Key Actions to Enable Automation for Mobile Network Security Operations

Over time, the landscape of Cyberspace surrounding Internet Service Providers (ISPs) has undergone enduring transformations. Notably, mobile networks, integral to contemporary societal infrastructure, consistently encounter evolving cybersecurity threats and risks. ISP processes have adapted with a persistent focus on optimizing network performance and availability, yet the challenges emerge from a laborious and protracted network change management process, hindering the practical automation of network security. Addressing the rightful demand for the highest level of security from mobile network users, our research question probes: "How can we intensify the emphasis on network security and facilitate the automation of network security operations?" To delve into this, we conducted extensive interviews with ISPs globally, affirming the inherent difficulty in automating security operations. The findings categorize challenges into three domains: Security Culture, Operational Processes, and Tools. Cultivating a security culture demands a pivotal commitment to change from top management, coupled with dedicated time and resources. Essential to this is the enhancement of security competence, extending beyond specialists to encompass network engineering staff. Robust network security not only safeguards against threats but significantly influences various business processes. Initiating a secure network requires ISPs to articulate explicit security requirements during the network procurement process, exerting pressure on vendors to fortify systems with a security-by-design approach at the factory. Critical to this is the secure deployment of networks, integrating comprehensive network hardening during the build phase. However, findings indicate a prevalent oversight where network security configuration changes are often neglected or deprioritized in favor of network performance. Achieving a harmonious balance between security and performance necessitates a predefined agreement on a network security configuration baseline. This collaborative effort involves network security specialists and competent network engineers. To effectively monitor and enforce network security configuration, ISPs require automation-enabled tools with the predefined baseline, offering capabilities for monitoring and enforcing network assets. In conclusion, our research emphasizes the imperative need for a paradigm shift in organizational culture, operational processes, and tool utilization to enhance the focus on network security and enable the critical automation of network security operations within the ever-evolving landscape of Cyberspace.

Enhancement Network Security Features Through Intrusion Detection Systems

Abstract: In pursuit of its objectives, the project "Enhancement Network Security features through Intrusion Detection Systems" undertakes a comprehensive exploration of the intricacies surrounding network security, delving into the nuances of intrusion detection methodologies. By dissecting the mechanisms behind both anomaly- and signature-based IDS, the initiative aims not only to implement these systems but also to grasp their underlying principles deeply. Through rigorous experimentation and analysis, the project endeavours to uncover the strengths and limitations of each approach, thereby paving the way for the development of a hybrid system that leverages the best of both worlds. This meticulous approach ensures that the resulting IDS is not only robust but also finely tuned to adapt to the ever-evolving landscape of cyber threats. Furthermore, the integration of machine learning techniques represents a pivotal advancement in the realm of intrusion detection. By harnessing the power of algorithms capable of autonomously learning from data, the IDS transcends static rule-based detection methods, ushering in a new era of adaptive security measures. Through continuous training on vast datasets comprising diverse threat scenarios, the machine learning-enabled IDS hones its ability to discern subtle patterns indicative of malicious activity, thus bolstering its efficacy in safeguarding network assets. Moreover, the implementation of machine learning algorithms fosters a proactive stance against emerging threats, enabling the system to anticipate and mitigate potential breaches before they materialize into fullfledged attacks. This proactive approach not only enhances the overall security posture of organizations but also instils confidence in their ability to stay ahead of the curve in an increasingly hostile digital landscape. The project's goal is to provide companies with a stronger protection against cyberattacks by guaranteeing the availability, confidentiality, and integrity of their networked systems.

An optimal expansion planning of power systems considering cycle-based AC optimal power flow

This paper presents a novel mixed-integer linear optimization formulation of the AC network-constrained, cost-based, integrated expansion planning problem. The formulation is used to determine the investment needs per technology including the location and sizing of new generation, energy storage, and transmission network assets in a future low-carbon power system. To reduce the size of the resulting problem, the AC optimal power flow (AC-OPF) model is represented in a compact way using cycle constraints. A bound tightening procedure is also considered to reduce the search space and improve the solver performance by adjusting the voltage bounds within the AC-OPF. Contrary to typically used formulations of the integrated expansion planning problem, the constraints considered here include all main aspects of system operation, namely unit commitment, energy storage system management, AC-OPF, and reactive power compensation. Thus, in this paper, we examine how both the proposed transmission expansion modeling developments and the interrelation of the integrated planning constraints affect the computation of the solution to the expansion planning problem. The performance of this formulation is assessed on the RTS-GMLC test system by computing the expansion plan and comparing it with the results of three other expansion planning formulations most frequently employed in the recent literature to address the integrated expansion planning problem for medium to large-scale systems. Expansion plans are computed and compared for different case studies and multiple scenarios. According to the comparative analysis, neglecting the AC-OPF or the unit commitment constraints can increase the total system costs by 7.10%–9.57% or 6.29%–8.39%, respectively. Unlike other modeling approaches, the proposed approach does not rely on simplifications that impact the quality of the solution. Thanks to the incorporated cycle-based AC-OPF constraints and the consideration of a bound tightening procedure, the computation time is reduced by 17.67%–27.21%.

Application Study on the Reinforcement Learning Strategies in the Network Awareness Risk Perception and Prevention

The intricacy of wireless network ecosystems and Internet of Things (IoT) connected devices have increased rapidly as technology advances and cyber threats increase. The existing methods cannot make sequential decisions in complex network environments, particularly in scenarios with partial observability and non-stationarity. Network awareness monitors and comprehends the network's assets, vulnerabilities, and ongoing activities in real-time. Advanced analytics, machine learning algorithms, and artificial intelligence are used to improve risk perception by analyzing massive amounts of information, identifying trends, and anticipating future security breaches. Hence, this study suggests the Deep Reinforcement Learning-assisted Network Awareness Risk Perception and Prevention Model (DRL-NARPP) for detecting malicious activity in cybersecurity. The proposed system begins with the concept of network awareness, which uses DRL algorithms to constantly monitor and evaluate the condition of the network in terms of factors like asset configurations, traffic patterns, and vulnerabilities. DRL provides autonomous learning and adaptation to changing network settings, revealing the ever-changing nature of network awareness risks in real time. Incorporating DRL into risk perception increases the system's capacity to recognize advanced attack methods while simultaneously decreasing the number of false positives and enhancing the reliability of risk assessments. DRL algorithms drive dynamic and context-aware response mechanisms, making up the adaptive network prevention component of the development. Predicting new threats and proactively deploying preventive measures, such as changing firewall rules, isolating compromised devices, or dynamically reallocating resources to reduce developing risks, is made possible by the system's ability to learn from historical data and prevailing network activity. The suggested DRL-NARPP model increases the anomaly detection rate by 98.3%, the attack prediction accuracy rate by 97.4%, and the network risk assessment ratio by 96.4%, reducing the false positive ratio by 11.2% compared to other popular methodologies.

Next-Generation Threat Detection and Mitigation in 6G Wireless Networks Using IAM, ZTNA and Advanced Security Mechanisms

As the deployment of 6G wireless networks looms on the horizon, the imperative to fortify their security infrastructure becomes increasingly pressing. One way to achieve this is through Identity and Access Management (IAM) frameworks to implement strong authentication and authorization mechanisms. Zero Trust Network Access (ZTNA) architectures advocate for a shift towards continuous verification and least-privileged access principles. Secure network segmentation and behavior anomaly detection systems limit the scope of potential breaches. Intrusion Detection and Prevention Systems (IDPS) detect and block cyber threats. These advanced security mechanisms improve the resilience of 6G networks and ensure the integrity, confidentiality, and availability of critical network assets. IAM protocols exhibit fast user authentication speeds with average authentication time of 0.5 seconds. ZTNA frameworks ensure high network security with a 98% detection rate of abnormal network behaviors

Next-Generation Threat Detection and Mitigation in 6G Wireless Networks Using IAM, ZTNA and Advanced Security Mechanisms

As the deployment of 6G wireless networks looms on the horizon, the imperative to fortify their security infrastructure becomes increasingly pressing. One way to achieve this is through Identity and Access Management (IAM) frameworks to implement strong authentication and authorization mechanisms. Zero Trust Network Access (ZTNA) architectures advocate for a shift towards continuous verification and least-privileged access principles. Secure network segmentation and behavior anomaly detection systems limit the scope of potential breaches. Intrusion Detection and Prevention Systems (IDPS) detect and block cyber threats. These advanced security mechanisms improve the resilience of 6G networks and ensure the integrity, confidentiality, and availability of critical network assets. IAM protocols exhibit fast user authentication speeds with average authentication time of 0.5 seconds. ZTNA frameworks ensure high network security with a 98% detection rate of abnormal network behaviors.

Критерии теплосетевых организаций: проблемы правоспособности

Amendments to the heat supply laws came into force over a year ago, providing criteria for acquiring the status of a heat network company. The purpose of these innovations was to bring order to the heat market and maintain competent professional players. It aimed at consolidating network assets in the last resort heat supplier, the unified heat distribution company, and depriving enterprises that lack the necessary material, labor, and technical resources for proper maintenance and repair of heat networks of the status of a heat network company. The intermediate results of the reform suggest now that the legislator’s expectations were not fully met. While disclosing the historical peculiarities of heat industry development in the pre-reform period, this article examines the factors that first led to the emergence of multiple heat network companies in the heat supply market and then prompted the legislator to take measures to optimize the number of regulated organizations. To simulate the potential effects of legal regulation, the author examines the electric power industry, which is related to heat supply. The legislator had already established criteria for local grid organizations in this industry several years before the reform of heat network companies. However, the legal position of the Constitutional Court of the Russian Federation prevented the legislator’s efforts from being fully implemented. To address the problem at hand, the author suggests transitioning from quantitative to qualitative criteria for evaluating an entity’s compliance with the status of a heat network company. This approach will prioritize the reliability of heat supply over using compliance as a formal basis for depriving bona fide enterprises of the status of a professional player in the heat market.

Attack graph-based stochastic modeling approach for enabling cybersecure semiconductor wafer fabrication

The increasing interconnectivity between information technology (IT) and operational technology (OT) has brought numerous benefits to advanced manufacturing, including the semiconductor industry, by enhancing the quality and by reducing the costs of wafer and integrated circuits (IC) fabrication. However, the transition of the semiconductor industry to secure digital manufacturing has also increased the importance of safeguarding networked assets from cybercriminals. To this end, a flexible scheme is developed in this paper to model wafer fabrication as a directed attack graph. This graph representation is then used as the basis of a two-stage stochastic programming model that considers uncertainty in attack probabilities while minimizing expected cyber risk by employing optimal, budget-constrained countermeasures (i.e., arc interdiction strategies). A sample average approximation coupled with a progressive hedging algorithm or Bender’s decomposition are proposed. Numerical experiments are performed to assess the viability of the proposed solution algorithms, the optimal arc interdiction strategies under various budget levels, and the effect of attack probability mappings. The results indicate that computing terminals nearest the manufacturing process have the highest defense priority and that the sample average approximation coupled with a progressive hedging algorithm is a suitable hybrid solution procedure for large instances when compared with sample average approximation coupled with Benders.

A Review of Uncertainties in Power Systems—Modeling, Impact, and Mitigation

A comprehensive review of uncertainties in power systems, covering modeling, impact, and mitigation, is essential to understand and manage the challenges faced by the electric grid. Uncertainties in power systems can arise from various sources and can have significant implications for grid reliability, stability, and economic efficiency. Australia, susceptible to extreme weather such as wildfires and heavy rainfall, faces vulnerabilities in its power network assets. The decentralized distribution of population centers poses economic challenges in supplying power to remote areas, which is a crucial consideration for the emerging technologies emphasized in this paper. In addition, the evolution of modern power grids, facilitated by deploying the advanced metering infrastructure (AMI), has also brought new challenges to the system due to the risk of cyber-attacks via communication links. However, the existing literature lacks a comprehensive review and analysis of uncertainties in modern power systems, encompassing uncertainties related to weather events, cyber-attacks, and asset management, as well as the advantages and limitations of various mitigation approaches. To fill this void, this review covers a broad spectrum of uncertainties considering their impacts on the power system and explores conventional robust control as well as modern probabilistic and data-driven approaches for modeling and correlating the uncertainty events to the state of the grid for optimal decision making. This article also investigates the development of robust and scenario-based operations, control technologies for microgrids (MGs) and energy storage systems (ESSs), and demand-side frequency control ancillary service (D-FCAS) and reserve provision for frequency regulation to ensure a design of uncertainty-tolerance power system. This review delves into the trade-offs linked with the implementation of mitigation strategies, such as reliability, computational speed, and economic efficiency. It also explores how these strategies may influence the planning and operation of future power grids.

Disruptions, technologically convergent factors and creative activities: defining and delineating musical stuff

ABSTRACT Ubiquitous music (ubimus) frameworks imply a flexible approach to the design, implementation and deployment of networked resources, featuring characteristics that challenge current conceptual groundings of music-making. As a potentially unifying place-holder for post-2020 musical practices enabled by ubiquitous technologies, musical stuff presents specific potentialities and caveats. We discuss the requirements for ubimus technological designs and analyze the possibilities and problems of adopting ‘stuff’ as a theoretical construct for ubimus endeavours. Several case scenarios tied to specific qualities are described, addressing aspects such as volatility, antirivalry, semantic strategies and fluidity, targeting the deterritorialization of resources and the application of aesthetic pliability. We highlight the challenges presented by the disruptive tendencies of fast or socially brittle technological change, with particular attention to the monetization of network assets. Stuff is proposed as nonrival, genre-neutral and pliable material for the development of a community-oriented musical internet.

Distributed Adaptive Robust Restoration Scheme of Cyber-Physical Active Distribution System With Voltage Control

The increasing penetration of distributed generators (DGs) and the advancement of information and communication technologies (ICTs) will facilitate the transformation of the traditional passive distribution network towards a cyber-physical active distribution system (CPADS). With the increasing risks of extreme events, such as natural disasters (e.g., flooding) and cyber-physical attacks, it is critical for CPADS to formulate a restoration scheme to improve its resilience. Therefore, in this paper, a distributed adaptive robust restoration scheme with voltage/var control is presented to cope with the high-impact but low-frequency events. First, a detailed cyber-physical system model is established, including the dynamic routing and the quality-of-services (QoS) in both optical fiber networks and 5G wireless networks. Then, the interactions between the cyber system and the physical system are analyzed. Based on the cyber-physical system model, a two-stage restoration scheme with voltage/var control is proposed by coordinately scheduling different network assets in day-ahead and in real-time. The formulated problem is solved by adaptive robust optimization (ARO). To further enhance the resilience of the CPADS, a distributed restoration framework is proposed. The distributed problem is solved by the alternating direction method of multipliers (ADMM) algorithm, and the convergence of the discrete problem is ensured by introducing the alternating optimization procedure (AOP). Considering the cyber faults, a boundary variable compensation and residual relaxation mechanism is proposed in ADMM. The proposed framework and methodology are verified in the case study. The convergence and the efficiency of the proposed algorithm are verified. Compared with the state-of-art works, the advantages in load restoration capability of the proposed method are shown.

Virtual Prosumers’ P2P Transaction Based Distribution Network Expansion Planning

The deployment of prosumers with distributed energy resources (DERs) has facilitated the prosumer-centric transactive market, giving rise to the consequent necessity for the coordination between prosumers and the distribution networks. This paper proposes an incentive-based expansion planning method considering virtual peer-to-peer (P2P) transaction among prosumers, which is characterized as a tri-level planning-operation-transaction framework. Each prosumer optimizes its internal response of DERs and external P2P transaction jointly, formulating virtual P2P transaction. Under such a paradigm, the distribution company (DISCO) identifies the optimal expansion planning in the network assets, while the independent distribution system operator (IDSO) is responsible for the optimal operation of the expanded distribution networks. To derive appropriate incentives for each prosumer, the DISCO and IDSO allocate long-term investment network charges and short-term operation network charges on prosumers, along with the designed financial transmission rights coupling multiple time scales. Through such incentives, self-profit driven virtual transactions can relieve operation problems and postpone expansion planning, acting as a short-term alternative. Moreover, a risk-based approach is proposed for prosumer to flexibly change strategy according to different risk preference. Finally, results from the case study demonstrate the superiority and effectiveness of the proposed method in the collaborative application of virtual transaction in the expansion planning.

Vinia: Voice-enabled intent-based networking for industrial automation

Intent Based Networking (IBN) is a promising approach for automating and managing large and complex networks. Integrating Voice-enabled Virtual Assistants (VVAs) with IBN and Software Defined Networking (SDN) has improved network management efficiency and flexibility. However, there is still room for optimization improvement in installing intents in industrial scenarios. Network Orchestration Automation plays an important role within the Beyond 5G and 6G Networks, considering existing practices for orchestrating 5G Network Functions. This work presents an extended preliminary architecture for a voice-enabled IBN system called VINIA for industrial network automation. The new approach allows the configuration of more network assets (e.g., 5G networks), leveraging Network Orchestrators and Network Slice Managers, thus improving the system?s capabilities. The results provide insights into this solution?s potential benefits and limitations to enhance the automation of industrial networks? management and orchestration procedures.

Power flow traceable P2P electricity market segmentation and cost allocation

This study explores peer-to-peer (P2P) electricity trading, emphasizing not just the export and consumption, but also the feasible physical supply of electricity and the use of distribution network assets. Building on a transaction-oriented dynamic power flow tracing model, a novel P2P market architecture is proposed. This architecture integrates the electricity market with the power network, considering technical constraints, network losses, and asset usage. The network is segmented into potential markets using second-order cone programming (SOCP), with an optimization problem introduced for loss-allocation. This problem merges network physical analysis and variable outputs from distributed energy resources (DERs). A graph-based P2P electricity trading model is designed to determine optimal transaction cost allocation and maximize benefits for both DERs and consumers. A case study on a modified IEEE 33-node test feeder substantiates the benefits of this market structure, demonstrating increased revenues for DERs and reduced bills for consumers compared to traditional feed-in-tariffs.

A comprehensive theoretical framework for sustainable network contracts: Contracting dimensions and Contract classification

Disruptions in the supply chains/networks result in both performance failures and a poor return on investment (ROI) of network assets. We propose that addressing this situation requires the governance of exchange relationships through contracts guided by sustainability principles. Specifically, we refer to these contracts in the supply and distribution context as “sustainable network contracts”, and the overall framework as the “sustainable contracting framework (SCF)/theory”. Despite the critical role of sustainable network contracts for all key stakeholders in the network, we identify a gap in the existing literature regarding the theory of sustainable network contracts. We bridge this gap by extending the extant dimensions of contracts, as described in Transaction Cost Economics and Relational Exchange Theory, to include sustainability dimensions - constituting the ‘what’ of theory building. To inform sustainable contracts, we propose and employ three factors: costs, benefits, and risks (CBR). We present the ‘how’ of the theory building, outlined in a five-step approach along with an analytical tool, to demonstrate the practical application of our framework. Additionally, we provide a rationale for adopting sustainable network contracts (the ‘why’ of the theory building). Our research methodology involves collecting interview-based data from senior executives (CXOs) (secondary and primary), collecting primary and secondary cost data (objective), integrating behavioral elements (subjective), and employing constrained optimization techniques to determine quantity allocation under various contract policies. Further, we map the proposed eight distinct contract types onto the CBR-space, thereby highlight the relevance of the contract types to real-world practices. This mapping considers network externalities, risks, and allows for a coordinated sustainable approach to contracting from the buyer's (retailer's) perspective. For managers, our framework would serve as an important tool that would inform the contract evaluation process, facilitate local versus global decision-making, safeguard network investments, and help align the interests of buyers and suppliers in each contracting cycle.

Data integration in asset management of municipal pipe networks in Sweden: Challenges, gaps, and potential drivers

This study involved a survey of Swedish water utilities to evaluate their pipe-network data-collection objectives, usage, storage, and exchange routines. Factors impacting data integration (and the associated benefits) were also identified. Results showed that current data storage and exchange routines can be augmented to support commonly identified objectives and data utilisation needs, especially in larger water utilities. Levels of awareness of the opportunities for and benefits gained through asset management processes and data integration varied between utilities. Further research on the benefits of data integration in pipe network asset management is required to develop an evidence base on benefits accrued in practice, especially considering metadata, the diversity of legacy systems still in operation, costs and policy use.