Security Plan Research Articles

Study of Radio Reconnaissance Receivers Designed on the Basis of Solid-state Filters

Radio intelligence systems are vital technological resources that contribute significantly to contemporary security, defense, and intelligence efforts. They enable the monitoring of adversaries' communication signals, the decryption of encrypted information, and the acquisition of strategic advantages. These systems are essential for military operations, counter-terrorism efforts, national security, and strategic planning. As radio intelligence continues to evolve, it is anticipated that more effective and multifunctional systems will emerge in the future.In modern electronic warfare, radio reconnaissance stations are essential for effective signal detection, classification, and geolocation of enemy communication and radar signals. Current advancements in radio reconnaissance technology focus on enhancing measurement accuracy and reliability, crucial for precise threat identification and situational awareness. Key improvements include implementing high-resolution digital signal processing algorithms, advanced filtering techniques, and real-time data fusion to increase detection precision across complex environments. Furthermore, there is a strategic emphasis on reducing the physical dimensions and weight of these stations to improve mobility and operational efficiency in diverse field conditions. Innovations in miniaturized components, lightweight materials, and compact antenna arrays are central to these design objectives, facilitating deployment in both manned and unmanned platforms. This approach not only optimizes the performance of reconnaissance stations but also broadens their applicability across various operational scenarios, making them integral to modern defense systems. In the article, the application of solid-state filters in the spectrum analyzers of radio reconnaissance receivers was considered in order to increase the measurement accuracy and reliability of radio reconnaissance stations, and to reduce their size and weight. In the article, the modeling of load-connected filter structures was conducted to calculate quasi-statistical potential diagrams, which allows for the optimization of their parameters and reduces the design time of devices based on these filters. The research work was carried out in military unit No. N.

Gig Workers: A Comprehensive Analysis of the Rise, Challenges, and Future of the Gig Economy

The gig economy, defined by temporary contracts, freelance employment, and independent contracting, has seen substantial growth in the global labour market during the last twenty years. Gig workers function independently of conventional full-time employment, enjoying flexibility but sometimes missing access to benefits like job security, healthcare, and retirement plans. This study examines the evolution of the gig economy, analysing the many forms of gig employment, its catalysts, the influence of technology, and its consequences for both workers and employers. The analysis will address the issues encountered by gig workers, such as economic volatility, insufficient legal safeguards, and the ambiguity of employer-employee dynamics. The study will analyse worldwide reactions, regulatory patterns, and the future direction of gig employment considering governmental changes and technology improvements.

How to assess measurement capabilities of a security monitoring infrastructure and plan investment through a graph-based approach

Security monitoring is a crucial activity in managing cybersecurity for any organization, as it plays a foundational role in various security processes and systems, such as risk identification and threat detection. To be effective, security monitoring is currently implemented by orchestrating multiple data sources to provide corrective actions promptly. Poor monitoring management can compromise an organization’s cybersecurity posture and waste resources. This issue is further exacerbated by the fact that monitoring infrastructures are typically managed with a limited resource budget. This paper addresses the problem of supporting security experts in managing security infrastructures efficiently and effectively by considering the trade-off cost-benefit between using specific monitoring tools and the benefit of including them in the organization’s infrastructure. To this aim, we introduce a graph-based model named Metric Graph Model (MGM) to represent dependencies between security metrics and the monitoring infrastructure. It is used to solve a set of security monitoring problems: (i) Metrics Computability, to assess the measurement capabilities of the monitoring infrastructure, (ii) Instrument Redundancy, to assess the utility of the instruments used for the monitoring, and (iii) Cost-Bounded Constraint, to identify the optimal monitoring infrastructure in terms of cost-benefit trade-off. We prove the NP-hardness of some of these problems, propose heuristics for solving them based on the Metric Graph Model and provide an experimental evaluation that shows their better performance than existing solutions. Finally, we present a usage scenario based on an instance of the Metric Graph Model derived from a state-of-the-art security metric taxonomy currently employed by organizations. It demonstrates how the proposed approach supports an administrator in optimizing the security monitoring infrastructure in terms of saving resources and speeding up the decision-making process.

Large scale system transformation: Ukraine

Abstract In the context of the EU-Ukraine Accession agreement, Ukraine started large scale public health systems reform back in 2015. The COVID-19 pandemic tested the response capacity of the national public health system and led to some improvement during the pandemic such as on laboratory capacity. It also generated lessons for strengthening national and subnational health emergency preparedness and response. Multiple interventions, including a Joint External Evaluation led to the adoption of the Public Health System Law in 2022, laying the foundation to bolster health promotion, disease prevention, and health protection. The Ukrainian Public Health Centre was substantially strengthened, partly oriented on models in other European countries. Corresponding centers for diseases control and prevention at the oblast level were reformed and a subnational network of these centers was created. While the war has a significant impact on the public health system, infrastructure, people and more, decision makers and professionals keep working on sustaining and moving forward the achievements on public health reforms, for example with the adoption of the National Health Strategy 2030 and the adoption of the National Health Security Plan. Further legal and strategic preparations are afoot to strengthen public health services governance capacity, financing, institutional innovation and development, and public health workforce development.

Fiscal Sustainability in India: an Assessment in Pre and Aftermath of Covid-19

The purpose of this research paper is to evaluate India's budgetary sustainability both before and after the COVID-19 pandemic. Global economies have been severely affected by the COVID-19 pandemic, and India is no exception. The country is facing significant fiscal issues as a result of rising costs for social security, health care, and economic stimulus plans. This study examines shifts in India's fiscal situation prior to the pandemic and the consequences encountered in its aftermath by examining fiscal variables such as state debt, budget deficits, and revenue trends. Using a mixed-method approach, the study combines qualitative evaluations derived from government reports and economic forecasts with quantitative data analysis. Initial results show that there has been a decline in fiscal indicators since COVID-19, which has been made worse by lower revenue generation and higher demands for expenditure.

Multilevel Factors Influencing the Requirement for Geriatric Nursing by Older Adults Living With HIV: A Cross-Sectional Study.

People living with HIV are aging. This study aimed to assess the factors influencing the requirements for geriatric nursing of older adults living with HIV (OALHIV). Convenience sampling was used to conduct a survey on the 295 OALHIV aged over 50 in Luzhou, China. OALHIV had few needs for living care needs. Most people indicate a requirement for reducing medical costs. Regarding psychological comfort needs, disease privacy and confidentiality were the greatest requirement. Multivariable regression analyses found that social support had a positive influence on the requirements for geriatric nursing. It is necessary to provide more social support for OALHIV. Most importantly, China should incorporate OALHIV into national pension security plan, integrate various resources and improve social security for them.

СТРАТЕГІЧНІ АСПЕКТИ УПРАВЛІННЯ ЕНЕРГЕТИЧНОЮ БЕЗПЕКОЮ ПРОМИСЛОВИХ ПІДПРИЄМСТВ В УМОВАХ МІНЛИВОСТІ ЗОВНІШНЬОГО СЕРЕДОВИЩА

The study is devoted to strategic planning and management of energy security of industrial enterprises in the conditions of dynamic changes in global energy markets and modern challenges. The relevance of the topic is due to the critical importance of energy security for ensuring the stability of production processes, optimizing costs, increasing energy efficiency and reducing the negative impact on the environment. At the theoretical level, the study analyzes the key concepts and methodologies underlying the formation of energy security management strategies. Special attention is paid to theoretical approaches to risk forecasting, threat identification, and the integration of the latest technologies, such as artificial intelligence and automated monitoring systems, into the strategic planning process. Theoretical analysis helps to reveal the main principles and strategies that are critical for adapting to a rapidly changing external environment and new threats. At the practical level, the study considers real cases and examples of the implementation of energy security strategies at industrial enterprises. Analyzes of best practices, emergency planning, crisis recovery, and resource management are included. The issue of coordination between different levels of management and integration of the energy security strategy with other corporate plans and state regulatory requirements is considered separately. The study emphasizes the need to create adaptive management systems that not only ensure the reliability of energy supply, but also respond promptly to various threats, including natural disasters, man-made accidents, terrorist acts and cyber attacks. The practical aspect of the research also includes evaluating the effectiveness of monitoring and feedback systems for adapting strategies based on changes in the external environment and monitoring results. The conducted research makes a significant contribution to the scientific discussion on the strategy and planning of energy security management, combining theoretical foundations with practical recommendations. Scientifically based methods of forecasting threats and developing adaptive strategies ensure effective management of energy security of industrial enterprises in the context of modern global challenges and technological changes.

Rice yield prediction through integration of biophysical parameters with SAR and optical remote sensing data using machine learning models

In an era marked by growing global population and climate variability, ensuring food security has become a paramount concern. Rice, being a staple crop for billions of people, requires accurate and timely yield prediction to ensure global food security. This study was undertaken across two rice crop seasons in the Udham Singh Nagar district of Uttarakhand state to predict rice yield at 45, 60 and 90 days after transplanting (DAT) through machine learning (ML) models, utilizing a combination of optical and Synthetic Aperture Radar (SAR) data in conjunction with crop biophysical parameters. Results revealed that the ML models were able to provide relatively accurate early yield estimates. For summer rice, eXtreme gradient boosting (XGB) was the best-performing model at all three stages (45, 60, and 90 DAT), while for kharif rice, the best-performing models at 45, 60, and 90 DAT were XGB, Neural network (NNET), and Cubist, respectively. The combined ranking of ML models showed that prediction accuracy improved as the prediction date approaches harvest, and the best prediction of yield was observed at 90 DAT for both summer and kharif rice. Overall rankings indicate that for summer rice, the top three models were XGB, NNET, and Support vector regression, while for kharif rice, these were Cubist, NNET, and Random Forest, respectively. The findings of this study offer valuable insights into the potential of the combined use of remote sensing and biophysical parameters using ML models, which enhances food security planning and resource management by enabling more informed decision-making by stakeholders such as farmers, policy planners as well as researchers.

Understanding the Root Cause of Cybersecurity Incidents Through DuPont’s Dirty Dozen Framework

Cybersecurity incidents, such as data breaches, pose a significant threat to organisations. Shockingly, 95% of these incidents occur due to human errors. Despite organisations making substantial efforts to reduce the likelihood of such occurrences through technological and non-technological means, the frequency of these incidents has been increasing. Previously, organisations relied on technology as the primary barrier to minimise cybersecurity incidents and achieve their objectives. Although research indicates that humans are the weakest link in an organisation's efforts to combat cybersecurity incidents, organisations still consider technology as the key to improving security defences. Therefore, the researchers suggest improving human interventions should precede technological means to overcome the problem. They propose that existing information security plans should consider human factors in cybersecurity risk management. Prioritising an understanding of human factors in managing information security can help organisations identify the relationships between various dimensions of human errors and cybersecurity incidents. To achieve this, the paper suggests solving the human factor problem in cybersecurity incidents by explaining how DuPont's Dirty Dozen framework, commonly used in aviation, can help understand why cybersecurity incidents and accidents occur. The framework lists twelve human behaviours that can be used to understand the relationships between various dimensions of human errors and cybersecurity incidents. By understanding these relationships, organisations can improve their cybersecurity strategies by anticipating, mitigating, and resolving issues more effectively and efficiently.

Personal data protection: Between human rights protection and national security

This study aimed to ascertain the equilibrium between safeguarding citizensʼ personal data and maintaining national security in a digital world. The research analysed the regulatory frameworks and judicial practices of the European Union (EU), Ukraine, and the USA through several methodologies. EU regulation offers the most stringent personal data protection, with substantial penalties for infractions. Ukrainian legislation is progressively aligning with European standards; however, procedures for protection and liability require enhancement. The research indicated an increasing tendency in the utilization of artificial intelligence and big data technologies within national security, presenting new issues for safeguarding personal information from disclosure. The research investigated the ethical implications of utilizing such technologies and their potential effects on citizen privacy. The study examined global regulatory procedures, focusing on the European Court of Human Rightsʼ approach to balancing the objectives of safeguarding personal information and national security. The research identified the necessity to broaden the definition of personal data to include communal dimensions and indirect ramifications of data processing in the context of big data and the Internet of Things. This studyʼs findings underscore the importance of an interdisciplinary approach to personal data security, encompassing legal, technological, ethical, and social dimensions. The analysis presented a conceptual model for harmonizing the regulatory framework for the protection of privileged information, including contemporary technical problems and national security requirements. The research holds practical importance for enhancing regulations regarding personal data protection and can assist the formulation of information security plans

A dynamic surface water extent service for Africa developed through continental-scale collaboration

Spatially explicit, near real time information on surface water dynamics is critical for understanding changes in water resources, and for long-term water security planning. The distribution of surface water across the African continent since 1984 and updated as every new Landsat scene becomes available is presented here, and validated for the continent for the first time. We applied the Water Observations from Space (WOfS) algorithm, developed and well-tested in Australia, to every Landsat scene acquired over Africa since the mid 1980s to provide spatial information on surface water dynamics over the past 30+ years. We assessed the accuracy of WOfS using aerial and satellite imagery. Four regional geospatial organisations, coordinated through the Digital Earth Africa Product Development Task Team, conducted the validation campaign and provided both the regional expertise and experience required for a continental-scale validation effort. We assessed whether the point was wet, dry, or cloud covered, for each of the 12 months in 2018, resulting in 34,800 labelled observations. As waterbodies larger than 100 km2 are easy to identify with Landsat resolution data and can thus boost accuracy, these were masked out. The resulting overall accuracy of the water classification was 82%. WOfS in Africa is expected to be used by ministries and departments of agriculture and water across the continent, by international organisations, academia, and the private sector. A large-scale collaborative effort, which included regional and technical skills spanning two continents was required to create a service that is regionally accurate and is both hosted on, and implemented operationally from, the African continent.

Governance of family food security and sustainable planning. The case of the municipality of Santiago de Cuba

Governance is a process that enables communication and cooperation between the government, other public institutions, social organizations, and families. In Cuba, the recent approval in 2020 of the Food Sovereignty and Nutrition Education Plan implies strengthening governance as a catalyst for family food security (SAF). However, incorporating sustainable planning to promote food security from dimensions that describe eating practices in Cuban homes still needs to be improved. This research aims to assess the links between governance and sustainable planning, to strengthen the dimensions of access, use, social capital, and cultural food capital, among others (SAF) in the Santiago de Cuba municipality. For this, the interview and survey methods are used. The first, to 20 families in a situation of social food vulnerability, and the second, to a sample of 300 households whose coexistence is not marked exclusively by the exposure mentioned earlier. The results point to poor communication among the social actors, generating food-destructuring practices. It is also visible the scarce insertion of these dimensions in planning to achieve greater family food sustainability. It is concluded that to achieve SAF, it is necessary to strengthen the interconnection of governance and sustainable planning from a relational perspective of the food field.

Implementing Privacy Preserving Techniques in Distributed Big Data Framework on Cloud Computing

Cloud-based deployment architectures have emerged as the preferred model for Big Data operations due to their scalability, flexibility, and cost-effectiveness. However, this shift raises new security concerns as data is no longer directly under the user's control. Securing Big Data in cloud environments is crucial for widespread adoption, but developing a comprehensive security plan is challenging without a thorough analysis of potential vulnerabilities.To address this, a novel security-by-design framework for Big Data deployment on cloud computing, termed Big Cloud, is proposed in this article. The framework leverages systematic security analysis methodologies and automated assessment tools to map domain-specific security knowledge to design best practices. Validation of the framework was conducted through an Apache Hadoop stack use case, demonstrating its effectiveness in enhancing security awareness and reducing design time.The study also evaluates the framework's strengths and limitations, identifying key challenges in the Big Cloud domain.

High-Level Isolation: A Landscape Analysis of Global Capabilities and Opportunities to Advance the Field.

High-level isolation units (HLIUs) have been established by countries to provide safe and optimal medical care for patients with high-consequence infectious diseases. We aimed to identify global high-level isolation capabilities and determine gaps and priorities of global HLIUs, using a multiple method approach that included a systematic review of published and gray literature and a review of Joint External Evaluations and Global Health Security Index reports from 112 countries. A follow-up electronic survey was distributed to identified HLIUs. The landscape analysis found 44 previously designated/self-described HLIUs in 19 countries. An additional 33 countries had potential HLIUs; however, there were not enough details on capabilities to determine if they fit the HLIU definition. An electronic survey was distributed to 36 HLIUs to validate landscape analysis findings and to understand challenges, best practices, and priorities for increased networking with a global HLIU cohort; 31 (86%) HLIUs responded. Responses revealed an additional 30 confirmed HLIUs that were not identified in the landscape analysis. To our knowledge, this was the first mapping and the largest ever survey of global HLIUs. Survey findings identified major gaps in visibility of HLIUs: while our landscape analysis initially identified 44 units, the survey unveiled an additional 30 HLIUs that had not been previously identified or confirmed. The lack of formalized regional or global coordinating organizations exacerbates these visibility gaps. The unique characteristics and capabilities of these facilities, coupled with the likelihood these units serve as core components of national health security plans, provides an opportunity for increased connection and networking to advance the field of high-level isolation and address identified gaps in coordination, build an evidence base for HLIU approaches, and inform HLIU definitions and key components.

An Approach to Secure Data Sharing in the Internet of Things Using Blockchain- Based Proxy Re-Encryption

The prevalence of data sharing has increased with the rise in popularity of cloud computing and the Internet of Things (IoT). However, ensuring data security is difficult because of the possible negative consequences of illegal data usage. This study presents a method for ensuring the secure transmission of data in cloud environments: proxy re-encryption. The identity-based encryption method enables data owners to transfer encrypted data to the cloud. Subsequently, a proxy re-encryption framework enables authorized individuals to access the data. To surpass the limitations of IoT device resources, we utilize an edge device as a proxy server to transfer computationally intensive tasks to a separate device. By utilizing the advantages of information-centric networking, we can optimize network capacity and enhance service quality by efficiently distributing cached content across the proxy. This method decreases bottlenecks in centralized systems while providing precise control over data access. The proposed work demonstrates the effectiveness of the strategy in ensuring data security, confidentiality, and integrity through thorough security research and plan review.

Analysis of Women's Participation in the Political and Social Processes of Afghanistan

Purpose: This study examines the implementation of UN Security Council Resolution 1325 on women, peace, and security in Afghanistan, focusing on the Islamic Republic of Afghanistan's action plan.Methodology:The research employs a mixed-methods approach, combining quantitative and qualitative analysis, and is a field study. The sample consisted of 100 well-known activists in women's participation studies and advocacy, chosen based on their professional experience and interactions over the past fifteen years. Data collection involved distributing 110 questionnaires with 35 closed-ended and three open-ended questions, resulting in 101 valid responses. The study utilized books, reports, surveys, and existing scientific studies to categorize and present the findings.Findings: The study revealed that since adopting the National Action Plan for Women, Peace, and Security to implement Resolution 1325, the Afghan government and national and international partners have undertaken various initiatives to enhance women's social and political participation.Implication:Numerous challenges and obstacles remain, necessitating severe attention from the government, legislative bodies, and their.

МОДЕЛИРОВАНИЕ НАРУШИТЕЛЯ, ИНФРАСТРУКТУРЫ И АТАК В СИСТЕМАХ ИНФОРМАЦИОННОЙ БЕЗОПАСНОСТИ

Formal models of subjects, infrastructure and attacks for information security systems are proposed. The models include descriptions of information security operators, administrators, users and violators, taking into account their knowledge, qualifications and initial conditions. A comprehensive intruder model is presented, including initial knowledge and access rights, initial location, qualifications and goals. Infrastructure models, vulnerabilities, and information collection methods are also considered, which makes it possible to more accurately predict the behavior of violators and develop effective protection strategies. The results of the study show that the proposed models significantly improve the accuracy of risk assessment and security planning, which is especially important for mission-critical information systems. The practical significance lies in the possibility of using models to develop and improve information network security systems. The results of the practical implementation of the model on real data are also presented.

Redefining Cyber Defense: The Evolution of Threat Detection with Artificial Intelligence

The cyber security industry is witnessing a significant shift as more and more companies look to artificial intelligence (AI) to rethink their defense strategies against emerging cyber threats. This study explores how artificial intelligence (AI) is fundamentally changing the way that threat detection paradigms are traditionally understood. Through an examination of the past and present of traditional approaches, we draw attention to the growing necessity of artificial intelligence-driven solutions. The paper provides a thorough examination of the basis of artificial intelligence (AI) in threat detection, with a focus on neural networks and machine learning methods. This paper examines several AI-powered threat detection methods, such as behavioral analysis, anomaly detection, and a comparison of heuristic-based and signature-based strategies. Examined are issues like explain ability, interpretability, and adversarial attacks, which offer a thorough understanding of the difficulties and factors, related to AI-driven cyber security. With a focus on threat intelligence platforms, explainable AI, predictive analytics, and integration, the paper takes a forward-looking approach to improving cyber security. The effectiveness and efficiency of AI-powered threat detection are compared to more conventional techniques in a comparative analysis that ends with advice for businesses looking to use AI-driven solutions. Key findings and their implications for the changing cyber defense landscape are summarized in the study&#39, s conclusion. By doing this research, we hope to add to the continuing conversation about the development of threat detection and provide useful advice and insights to help enterprises successfully incorporate AI into their cyber security plans.

A quasi-static time-series approach to assess the impact of high distributed energy resources penetration on transmission system expansion planning

Due to advancements in solar cell fabrication and inverter-based technology, as well as decreasing acquisition costs, solar distributed generation systems have emerged as a promising renewable source. Furthermore, electrification of the transportation system is an alternative promoted by many governments to mitigate the dependency on fossil fuels and achieve the goals of decarbonizing the economy. However, interconnecting a large quantity of distributed energy resources (DERs) to the power system reveals technical problems affecting the entire system. Planning coordinators and grid operators need to understand the full-spectrum impact of DERs on the power system to ensure secure and reliable grid planning and operations. The operation of a Hydro-Québec transmission system planned for 2030 is simulated in quasi-static time series mode with a massive integration of 1 million of electric vehicles and 1000 – 3000 MW of distributed photovoltaics. This paper provided findings on assessment of high DER penetrations impacts on the transmission system in terms of voltage control, mitigation means used, MVAR availability and consumption margin, generating unit optimal operation, and switching actions of several equipment.

Ngulluk Moort, Ngulluk Boodja, Ngulluk Wirin (our family, our country, our spirit): An Aboriginal Participatory Action Research study protocol.

Globally, Indigenous children have historical and contemporary connections with government child protection services that have caused significant harm to their long-term health and wellbeing. Innovative, culturally secure and recovery focussed service provision is required. This paper describes a research protocol that has been designed by Indigenous researchers led by Indigenous Elders, to explore culturally secure care planning and service delivery in out-of-home care agencies in Australia. Using participatory action research methods, we will collect data using a variety of forums, including focus groups and semi-structured interviews. These data will explore the challenges for out-of-home care agencies in providing culturally secure care-planning, cultural activity and resources, and explore solutions to address factors that influence health and can assist to redress social inequities for Indigenous children. We aim to recruit approximately 100 participants for the qualitative study and 40 participants for the quantitative survey. Study participants will initially be recruited using purposive sampling, and as the study progresses will be recruited using a mixture of purposive and convenience sampling techniques. The rich data that this study is expected to yield, will inform ways to collect cultural information about Indigenous children and ways to provide cultural connections and activities that will have benefit to Indigenous children and families, and a broad range of social services.