Critical Assets Research Articles

TRANSFER OF INTELLECTUAL PROPERTY RIGHT AS A COMPANY ASSET IN BANKRUPTCY IN INDONESIA

The transfer of intellectual property rights (IPR) as a company asset during bankruptcy proceedings in Indonesia presents a complex legal landscape. This study examines the gap between existing legal frameworks and the practical realities faced by companies undergoing bankruptcy. Specifically, the research addresses the lack of clear guidelines on the valuation, transfer, and protection of IP rights, which are critical assets in the modern economy. This study utilizes the three research methods, which are Normative Approach and Conceptual Approach. Findings indicate significant inconsistencies and ambiguities in the legal treatment of IPR in bankruptcy cases. The aim of this research is to provide an understanding of how IPR can be included in the bankruptcy estate and distributed to entitled creditors based on an assessment by the curator. In conclusion, this research found that recommendations for legal amendments and policy interventions to bridge the identified gaps, thereby enhancing the protection and transferability of IP rights in bankruptcy contexts in Indonesia.

Data Resilience: Secure Emergency Backup on Cloud

In the dynamic landscape of cloud computing, ensuring the resilience and availability of data is paramount, particularly in emergency situations such as cyberattacks, system failures, or natural disasters. This abstract presents a comprehensive examination of emergency backup strategies tailored specifically for cloud environments. It delves into the challenges associated with data loss and downtime, emphasizing the critical need for proactive measures to mitigate risks and safeguard valuable data assets. By exploring various backup methodologies including incremental backups, differential backups, and snapshot-based backups, this abstract provides insights into the diverse approaches available for creating robust data redundancy in the cloud. Furthermore, it discusses the importance of geographical redundancy, data encryption, and access controls in enhancing the security and integrity of backup data. Additionally, emerging technologies such as blockchain-based backup solutions and AI-driven anomaly detection systems are explored as potential avenues for further enhancing data resilience in the cloud. By adopting a proactive approach to emergency data backup, organizations can fortify their cloud infrastructures against unforeseen disruptions and ensure the continuous availability and integrity of their critical data assets

The Role of Artificial Intelligence in Cyber Security

The research paper explores the pivotal role of artificial intelligence (AI) in bolstering cyber security threat detection and protection measures. By leveraging advanced AI techniques such as machine learning, deep learning, and behavioral analysis, organizations can effectively identify and mitigate cyber threats in real-time. AI enables proactive threat detection by analyzing vast amounts of data, detecting patterns, and predicting potential security incidents. Moreover, AI-driven security solutions offer scalability, efficiency, and adaptability, making them indispensable in combating the evolving cyber threat landscape. Overall, AI plays a crucial role in fortifying cyber security defenses and safeguarding critical assets against sophisticated cyberattacks. As cyber threats continue to grow in complexity and frequency, the integration of AI into security frameworks will not only enhance response times but also empower organizations to stay one step ahead of potential attacks Furthermore, as organizations increasingly adopt AI-driven security measures, the importance of integrating ethical considerations and addressing potential biases in AI algorithms becomes paramount. While these technologies can significantly enhance threat detection capabilities, they also risk perpetuating existing inequalities if not meticulously designed and monitored. For instance, biased data sets may lead to disproportionate scrutiny of certain user groups, undermining trust in security systems. Additionally, the dynamic nature of cyber threats necessitates continuous learning and adaptation from AI systems, highlighting the need for ongoing research and collaboration among cybersecurity professionals, technologists, and ethicists alike. By fostering a holistic approach that emphasizes both technological advancement and ethical responsibility, organizations can better navigate the complexities of modern cyber environments while enhancing their overall security posture

Intrusion Detection System

Our project introduces an Integrated Intrusion Detection System (IDS) designed to bolster network and system security through a multi-faceted approach. This comprehensive IDS seamlessly combined Machine Learning, Anomaly Detection, File Scanning, and DDoS Prevention mechanisms to offer robust defense against diverse cyber threats. At the heart of the system lies the Machine Learning component, which continuously adapts to the evolving threat landscape. By analyzing network traffic and system behavior, it identifies both known and emerging threats in real-time, reducing reliance on traditional signature-based detection methods and allowing organizations to proactively stay ahead of cybercriminals. Anomaly Detection constantly monitors network and system activity, comparing it against established baselines. Any deviations from these baselines trigger alerts, facilitating swift responses to unusual activities that may indicate security breaches. File Scanning is another vital component ensuring data integrity and preventing malware infiltration or data exfiltration. It conducts thorough file analysis, checking for suspicious code, behavior, or unauthorized access, and offers continuous monitoring to detect anomalies in real-time. Additionally, our IDS includes Distributed Denial of Service (DDoS) Prevention mechanisms. By detecting and mitigating DDoS attacks, the system ensures the continuous availability of network resources and services even under intense traffic loads. This integrated approach to intrusion detection and prevention leverages Machine Learning, Honeypots, Anomaly Detection, File Scanning, and DDoS Prevention, empowering organizations to safeguard critical assets, maintain data integrity, and ensure network security in today's dynamic and perilous digital landscape. Our IDS solution serves as a valuable addition to the cybersecurity toolkit for organizations seeking comprehensive security against a wide range of threats.

Generalized control for distributed energy inverter in community microgrid system: Off-grid and grid-connected operation

With the increasing penetration of distributed energy resources (DER) in microgrids, DER power inverters have become a critical asset for providing power support to these microgrids. Meanwhile, the grid-forming (GFM) inverters, among these DER inverters, have gained significant attention in microgrid applications for their capability to enable the DERs to operate in different microgrid conditions and various operation modes. Moreover, with the implementation of these GFM inverters, smooth operation mode transition, GFM functions as well as black start functions can be obtained to improve the operation of the microgrid systems. In this article, a generalized control method for a single-phase GFM inverter is developed for community microgrid applications, facilitating smooth operation behavior in both operation modes with grid support functions and stable transition for different microgrid conditions. The control design procedure and function analysis of the proposed control method are explained in detail based on the community microgrid system. The effectiveness of the method in this paper is demonstrated on a 10 kW single-phase GFM inverter prototype with comparison to a model predictive method in recent literature.

Electrifying the Last-Mile Logistics (LML) in Intensive B2B Operations—An European Perspective on Integrating Innovative Platforms

Background: literature on last mile logistic electrification has primarily focused either on the stakeholder interactions defining urban rules and policies for urban freight or on the technical aspects of the logistic EVs. Methods: the article incorporates energy sourcing, vehicles, logistics operation, and digital cloud environment, aiming at economic and functional viability. Using a combination of engineering and business modeling combined with the unique opportunity of the actual insights from Europe’s largest tender in the automotive aftermarket electrification. Results: the Last Mile Logistics (LML) electrification is possible and profitable without jeopardizing the high-tempo deliveries. Critical asset identification for a viable transition to EVs leads to open new lines of research for future logistic dynamics rendered possible by the digital dimensions of the logistic ecosystem. Conclusions: beyond the unquestionable benefits for the environment, the electrification of the LML constitutes an opportunity to enhance revenue and diversify income.

Assessing the attractiveness of chemical and process facilities to terrorism using a situational crime prevention approach

As intentional attacks to industry have been on the rise in the last years, researchers and institutions put effort in addressing risks of industrial assets exposed to acts of interference. One of the key steps of security risk assessment is the evaluation of the attractiveness, i.e. determining which installation or part of it could be of interest to a potential attacker. As security concerns are intrinsically tied to human intention, attractiveness does not only depend on technical characteristics of the facility, but also on the type of threat and its geographic, economic, and socio-political context. Therefore, interdisciplinarity is among the key features of a team tasked with assessing attractiveness. This work is the result of the joint effort of Physical Sciences and Engineering experts and Social Sciences and Humanities experts, and it presents a methodology for the assessment of attractiveness of process facilities to physical terroristic attacks developed using a “terrorist thinking” approach. Technical factors such as plant layout and physical protection systems were combined with modern criminology principles (namely Situational Crime Prevention, i.e. how actors take advantage of situated opportunities to commit a crime) to obtain a comprehensive picture of the possible motives behind a physical terroristic attack to a chemical and process facility (meant as facilities where chemical substances are present and where chemical and/or physical processes occurs). The methodology was applied to a case study featuring facilities located in a critical context; a ranking among facilities was obtained, and the robustness of the results was tested through a Monte Carlo-based uncertainty analysis. The results revealed that it is not possible to obtain a clear ranking of critical facilities accounting only for technical or non-technical factors, but both are necessary. The method developed was demonstrated easy to apply and could be used not only by plant security managers, but also by Institutions and Governments to prioritize critical assets in threatening contexts.

A Reappraisal on Link between Goodwill and Financial Performance in Sport Industry of Jalandhar

This study presents a comprehensive reconsideration of the subtle relationship between goodwill and financial performance in the sports industry. Traditionally, goodwill has been linked to intangible assets like brand reputation and consumer loyalty, which are difficult to measure. The study explores how goodwill, which is continuously reflected through brand reputation, fan loyalty, functions as a critical asset in improving financial outcomes for sports companies. By evaluating multiple revenue streams like sponsorships, retail sales, broadcast rights, and match day revenues, the study illustrates the mutually beneficial for a goodwill foundation and improved financial success. Furthermore, the study explores the moderating impacts of external factors such as market dynamics, competitive success, and digital media presence, providing a more nuanced picture of how goodwill contributes to long-term financial growth in the sports industry. It investigates how intangible assets like goodwill can be translated into actual financial rewards such as larger sponsorships, bigger ticket sales, and higher television ratings. The study's findings are especially pertinent for sports business owners and industry experts who want to manage the intricacies of managing a team or event in an increasingly competitive market. This reappraisal intends to broaden the debate on sports management and provide concrete lessons for practitioners seeking to harness goodwill as a strategic asset for financial success. Key words: Goodwill, Financial Performance, Sports Industry, Brand Reputation, Fan Loyalty, Sponsorships.

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.

Inducing Populace Rapport Around Bellandur Lake in Order to Revitalize and Protect Its Natural Environment

Urbanization is a global phenomenon with profound consequences for the environment. As cities expand and populations grow, the impact of urbanization on natural resources, including freshwater bodies, becomes increasingly evident. Bellandur Lake, once a pristine natural asset, now faces severe environmental challenges, including pollution and encroachment. This research study explores the imperative need to induce rapport within the local populace to revitalize and protect the natural environment of Bellandur Lake. Amidst ongoing trends, Bangalore grapples with a dire water scarcity. This research is crucial now, promising broader insights and solutions. The research begins with a comprehensive assessment of the current state of the lake, encompassing water quality, ecosystem health, and surrounding environmental conditions. It identifies the multifaceted environmental threats and challenges confronting the lake and investigates the existing level of public awareness and perception regarding these issues. Furthermore, the research study delves into an examination of existing policies and governance structures related to the lake and provides recommendations for their enhancement to ensure comprehensive environmental conservation. It also establishes a monitoring and evaluation framework to assess progress and sustainability in lake revitalization. This research study not only serves as a comprehensive guide to the revitalization and protection of Bellandur Lake but also as a model for inducing populace rapport around critical natural assets elsewhere. By nurturing a strong connection between the local populace and the lake, it is aimed to ensure the long-term well-being of both the ecosystem and the community.

Implementing Industry 4.0: An In-Depth Case Study Integrating Digitalisation and Modelling for Decision Support System Applications

The scientific community has shown considerable interest in Industry 4.0 due to its capacity to revolutionise the manufacturing sector through digitalisation and data-driven decision-making. However, the actual implementation of Industry 4.0 within complex industrial settings presents obstacles that are typically beyond the scope of mainstream research articles. In this paper, a comprehensive case-study detailing our collaborative partnership with a leading medical device manufacturer is presented. The study traces its evolution from a state of limited digitalisation to the development of a digital intelligence platform that leverages data and machine learning models to enhance operations across a wide range of critical machines and assets. The main business objective was to enhance the energy efficiency of the manufacturing process, thereby improving its sustainability measures while also saving costs. The project encompasses energy modelling and analytics, Fault Detection and Diagnostics (FDD), renewable energy integration and advanced visualisation tools. Together, these components enable informed decision making in the context of energy efficiency.

A QUANTITATIVE APPROACH TO ASSESSING AND MANAGING CYBERSECURITY RISKS

Effective cybersecurity risk management requires moving beyond traditional qualitative approaches toward quantitative methods that provide greater precision and objectivity. This article explores the benefits of cyber risk quantification (CRQ), which allows organizations to numerically estimate the likelihood of potential cyber threats and calculate their financial or operational impacts. By translating risks into quantifiable terms, cyber risk quantification (CRQ) provides a standardized framework to prioritize risks based on impacts, evaluate mitigation strategies through cost-benefit analysis, and align cybersecurity practices with business objectives. The quantitative, risk-based approach elevates cybersecurity as an integral part of enterprise risk management focused on protecting critical assets. Despite its analytical rigor, the quantification process leverages proven techniques to simplify risk data into clear metrics. This enables more informed decision-making by eliminating ambiguity, reducing subjective bias, and communicating risks transparently across stakeholders using a common language of risk values.

Securing the digital supply chain: Cybersecurity best practices for logistics and shipping companies

The review investigates the pressing need for robust cybersecurity measures within the logistics and shipping sector, where the digital supply chain is vulnerable to a myriad of cyber threats. The paper delves into the specific challenges faced by logistics companies, including the interconnectedness of global supply chains, reliance on digital technologies for operations, and the high value of goods in transit. It explores the multifaceted nature of cyber risks, encompassing threats such as ransomware, phishing attacks, data breaches, and supply chain disruptions, which can have far-reaching consequences for business continuity and reputation. Through a detailed analysis, the study elucidates cybersecurity best practices tailored to the logistics and shipping industry, encompassing both technical solutions and organizational policies. These include implementing robust authentication and access controls, encrypting sensitive data in transit and at rest, establishing secure communication channels, and conducting regular vulnerability assessments and penetration testing. Furthermore, the paper emphasizes the importance of fostering a culture of cybersecurity awareness among employees through comprehensive training programs and incident response drills. It also discusses the role of regulatory compliance frameworks such as GDPR, CCPA, and industry-specific standards like ISO 27001 in guiding cybersecurity efforts and ensuring adherence to best practices. By providing actionable recommendations and insights garnered from real-world case studies, the study equips logistics and shipping companies with the knowledge and tools needed to bolster their cybersecurity defenses, safeguard critical assets, and maintain trust in the digital supply chain ecosystem.

Cybersecurity on a budget: Evaluating security and performance of open-source SIEM solutions for SMEs.

The proliferation of cyber threats necessitates robust security measures to safeguard critical assets and data in today's evolving digital landscape. Small and Medium Enterprises (SMEs), which are the backbone of the global economy are particularly vulnerable to these threats due to inadequate protection for critical and sensitive information, budgetary constraints, and lack of cybersecurity expertise and personnel. Security Information and Event Management (SIEM) systems have emerged as pivotal tools for monitoring, detecting, and responding to security incidents. While proprietary SIEM solutions have historically dominated the market, open-source SIEM systems have gained prominence for their accessibility and cost-effectiveness for SMEs. This article presents a comprehensive study focusing on the evaluation of open-source SIEM systems. The research investigates the capabilities of these open-source solutions in addressing modern security challenges and compliance with regulatory requirements. Performance aspects are explored through empirical testing in simulated enterprise-grade SME network environments to assess resource utilization, and real-time data processing capabilities. By providing a rigorous assessment of the security and performance features of open-source SIEM systems, this research offers valuable insights to cybersecurity practitioners, organizations seeking cost-effective security solutions, and the broader academic community. The findings shed light on the strengths and limitations of these systems, aiding decision-makers in selecting the most suitable SIEM solution for their specific requirements while enhancing the cybersecurity posture of SMEs.

Review of Ground Penetrating Radar Applications for Bridge Infrastructures

Infrastructure bridges play a crucial role in fostering economic and social development. However, the adverse effects of natural hazard and weather degradation, coupled with escalating rates of traffic, pose a significant threat. The resultant strain on the structure can lead to undue stress, elevating the risk of a critical asset failure. Hence, non-destructive testing (NDT) has become indispensable in the surveillance of bridge infrastructure. Its primary objectives include ensuring safety, optimizing structural integrity, minimizing repair costs, and extending the lifespan of bridges. NDT techniques can be applied to both existing and newly constructed bridge structures. However, it is crucial to recognize that each NDT method comes with its own set of advantages and limitations tailored to specific tasks. No single method can provide an effective and unequivocal diagnosis on its own. Among the various NDT methods, Ground Penetrating Radar (GPR) has emerged as one of the most widely employed techniques for monitoring bridges. In fact, recent technical regulations now mandate the use of GPR for bridge monitoring and characterization, underscoring its significance in ensuring the structural health and longevity of these critical infrastructures. Ground Penetrating Radar (GPR) stands out as one of the most highly recommended non-destructive methods, offering an efficient and timely assessment of the structural conditions of infrastructure. Recognizing the pivotal role of non-destructive testing (NDT) in this context, this paper aims to elucidate recent scientific endeavors related to the application of GPR in bridge engineering structures. The exploration will commence with a focus on studies conducted both at the model level within laboratory settings and on real cases. Subsequently, the discussion will extend to encompass the characterization and monitoring of the bridge’s main elements: slab, beam, and pillar. By delving into these scientific experiences, this paper intends to provide valuable insights into the efficacy and applicability of GPR in assessing and ensuring the structural integrity of bridges. This paper provides a concise survey of the existing literature on the application of Ground Penetrating Radar (GPR) in the assessment of bridges and viaducts constructed with masonry and reinforced concrete, taking into account papers of journal articles and proceedings available on open databases. Various approaches employed in both laboratory and field settings will be explored and juxtaposed. Additionally, this paper delves into discussions on novel processing and visualization approaches, shedding light on advancements in techniques for interpreting GPR data in the context of bridge and viaduct evaluations.

Resilient–vulnerability analysis of critical infrastructure, key resources, assets, and facilities

Resilient–vulnerability analysis of critical infrastructure, key resources, assets, and facilities

Practical Application of Digital Twins for Transportation Asset Data Management: Case Example of a Safety Hardware Asset

Digital Twins have emerged as a transformative technology with immense potential for enhancing asset data management in various industries. The transportation industry relies heavily on properly functioning and maintaining safety hardware assets, such as crash barriers, guiderails, and road signs, among others, to ensure the safety of road users. However, traditional asset data management practices for ancillary assets often fall short of providing real-time, comprehensive insights into the condition and performance of these critical assets. Conversely, Digital Twins can analyze data for predictive and preventive maintenance using cutting-edge technologies. This will enable asset managers to proactively address potential safety concerns, plan maintenance schedules efficiently, and optimize resource allocation. This can be achieved by leveraging historical asset data and real-time condition updates and providing the Digital Twin with an information-rich analytical model. The key steps to creating an information-rich analytical model are identifying the asset data that should be collected, understanding data architecture throughout the asset lifecycle, and generating asset data governance and stewardship frameworks. As such, this paper investigates the current state of practice for ancillary asset data collection and management and investigates the generation of information-rich analytical models for guiderails, a critical roadway safety hardware. Moreover, this study demonstrates how Digital Twins can be integrated and affect the development of transportation asset management systems. The findings of this article emphasize the significance of adopting Digital Twins in transportation asset data management. The case example showcases a compelling roadmap for other transportation agencies to consider integrating digital twins into their asset management strategies.

The Rise of Passive RFID RTLS Solutions in Industry 5.0.

In today's competitive landscape, manufacturing companies must embrace digital transformation. This study asserts that integrating Internet of Things (IoT) technologies for the deployment of real-time location systems (RTLS) is crucial for better monitoring of critical assets. Despite the challenge of selecting the right technology for specific needs from a wide range of indoor RTLS options, this study provides a solution to assist manufacturing companies in exploring and implementing IoT technologies for their RTLS needs. The current academic literature has not adequately addressed this industrial reality. This paper assesses the potential of Passive UHF RFID-RTLS in Industry 5.0, addressing the confusion caused by the emergence of new 'passive' RFID solutions that compete with established 'active' solutions. Our research aims to clarify the real-world performance of passive RTLS solutions and propose an updated classification of RTLS systems in the academic literature. We have thoroughly reviewed both the academic and industry literature to remain up to date with the latest market advancements. Passive UHF RFID has been proven to be a valuable addition to the RTLS domain, capable of addressing certain challenges. This has been demonstrated through the successful implementation in two industrial sites, each with different types of tagged objects.

Mitigating ransomware attacks through cyber threat intelligence and machine learning

<div>In the face of escalating cyber threats, particularly the rampant and sophisticated nature of ransomware attacks, organizations are compelled to adopt a proactive and multi-faceted strategy for mitigation. The fusion of machine learning (ML) algorithms enables the system to dynamically adapt and evolve in response to evolving attack vectors and tactics employed by cybercriminals. This paper presents a comprehensive approach that synergistically integrates ML and cyber threat intelligence (CTI) to fortify defenses against ransomware assaults. The proposed methodology incorporates three distinct machine learning techniques, namely random forest (RF), extreme gradient boosting (XGBoost), and adaptive boosting (AdaBoost). Empirical evidence derived from the study affirms the efficacy of this approach in effectively discriminating between malicious and ransom software, achieving a notable identification rate of 98.55%. The incorporation of CTI enhances the strategic posture by providing actionable insights into the threat landscape. The proposed focuses on identifying and neutralizing ransomware, aligning with contemporary cybersecurity imperatives, offering a proactive defense against ransomware attacks, ultimately safeguarding critical assets, and preserving the integrity of digital ecosystems.</div>