Security Management Research Articles

Implementation of Facility and Safety Management at Public Health Center Pesantren II Kediri

Health Health centers must be accredited to improve the quality of health services. In order for accreditation to be achieved, Public Health Center has requirements that must be met regarding buildings, infrastructure, equipment, and providing a safe environment for service users, visitors, officers, and the community, including patients with physical limitations. Patients with physical limitations are given access to obtain services, so facility and safety management are needed. Community service activities carried out by the Postgraduate Program in Public Health IIK Strada Indonesia, which was carried out from May 22, 2023, to June 17, 2023, aim to identify the implementation and implementation strategies for facility management and safety using Fishbone, USG, and SWOT analysis at Public Health Center Pesantren II Kediri. One of the implementations was the socialization of facility and safety management to all employees, and the results of the Wilcoxon statistical test pre-test and post-test showed a sig value. 0.000 (<0.05) means that there is an effect of socialization of facility management and security among Public Health Center Pesantran II employees. Implementation and management strategies for facilities and security management at Public Health Center Pesantren II Kediri have been well implemented. To improve service quality, there are several conditions that can be optimized. Public Health Center management has a big role to play in ensuring the management of facility management and security in accordance with applicable regulations.

“The role of modern technology for safety and security management in construction sites in Kuwait”

The research aimed to investigate the significance of modern safety techniques in the construction industry of Kuwait. It found a lack of awareness among workers and organizations, hindering their implementation. The study highlighted the benefits of using modern safety technologies to address safety issues and recommended training workers, government support, and the involvement of professional institutions to raise awareness

Hybrid software defined network-based deep learning framework for enhancing internet of medical things cybersecurity

<p>The risk of cyber-attacks has increased significantly with the rapid development of the Internet of Medical Things (IoMT). The proliferation of IoMT devices in healthcare facilities has made conventional intrusion detection approaches challenging to employ. Our study proposes a novel hybrid framework leveraging Software Defined Network (SDN) controllers and deep learning techniques, specifically Convolutional Neural Networks (CNN) and Bidirectional Long-Term Memory (Bi-LSTM), to address these challenges. Our framework introduces a unique combination of SDN and deep learning, allowing for dynamic and efficient management of IoMT security. The integration of CNN and Bi-LSTM enables the system to handle diverse data types encountered in IoMT, offering a comprehensive approach to threat detection. Unlike traditional methods, our hybrid solution adapts seamlessly to the evolving threat landscape of healthcare IoT systems. The urgency of our research is affirmed by the critical need to fortify IoMT security amid escalating cyber threats. The conventional methods struggle to cope with the complex nature of IoMT networks, making our exploration of a hybrid SDN-based deep learning framework imperative. With a background in cybersecurity and a dedicated focus on healthcare IoT, we recognize the urgency to develop a solution that not only enhances detection accuracy but also ensures real-time responsiveness in healthcare settings. The proposed method has been validated using the “IoT-Healthcare security” dataset, revealing its efficacy in detecting numerous frequent threats and outperforming current state-of-the-art techniques in terms of high detection accuracy of 99.97% and less than 1.8 (s) in terms of speed efficiency.</p>

Real-Time Intrusion Detection in Power Grids Using Deep Learning: Ensuring DPU Data Security

Deep learning technologies have revolutionized the management of energy, energy consumption, and data security within smart grids through non-intrusive load monitoring (NILM). This paper explores the use of deep learning for real-time intrusion detection in power grids with a primary focus on safeguarding the integrity and security of Data Processing Units (DPUs). An evaluation of various machine learning models, including Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Decision Trees, and Random Forests, is conducted to detect various types of intrusions, including Fault, Injection, Masquerade, Normal, and Replay. Random Forest produced AUC values of 1.00 for all classes and an overall F1-score of 0.99 for all classes. The Decision Tree model also shows robust performance for detecting Fault and Injection intrusions (AUC = 0.98), with an overall F1-score of 0.94. However, the LDA and SVM models do not perform well in detecting Injection intrusions with overall F1-scores of 0.83 and 0.86. Advances in machine learning can be used to improve smart grid security, reliability, and efficiency, according to this study. These findings highlight the potential of advanced machine learning techniques to enhance smart grid reliability and efficiency. Doi: 10.28991/HIJ-2024-05-03-018 Full Text: PDF

Analysis of Archives Storage Systems in the Logistics Division at PT. Dharma Lautan Utama Surabaya

This research is a qualitative research. The research methods used include document analysis, interviews, and direct observation in the field. This research aims to analyze the archival storage system in the logistics division of PT. Dharma Lautan Utama Surabaya so that archived documents can be stored properly and optimally. The research results show that the archive storage process currently used still faces challenges in terms of information accessibility, document security, and archive storage space management. Some of the efforts suggested in this research are related to implementing a digital archives management system, efficient spatial planning, using minimalist archive storage facilities, and employee training regarding document management.

Blockchain-Integrated Security Framework for Fog Networking of Connected Vehicles

In the rapidly evolving domain of connected vehicles, ensuring robust security and efficient data management is paramount. Traditional centralized architectures struggle with latency, scalability, and vulnerability to single points of failure. This research proposes a novel Blockchain-Integrated Security Framework (BISF) for fog networking in connected vehicles. The BISF leverages blockchain technology to enhance data integrity, transparency, and security while utilizing fog computing to reduce latency and improve real-time data processing. The proposed framework integrates smart contracts for automated trust management and distributed ledgers for immutable data records. Simulation results demonstrate the effectiveness of BISF in enhancing security, reducing latency, and improving overall network performance compared to traditional centralized approaches.

THE IMPACT OF MARTIAL LAW ON MANAGEMENT DECISIONS REGARDING ENSURING THE FINANCIAL SECURITY OF ENTERPRISES

The aim of this article is to present an approach to assessing the most significant negative factors that have arisen as a result of the introduction of martial law on the decision-making system and the implementation of managerial decisions in the context of ensuring the financial security of a modern enterprise. For this purpose, the object of research is the financial security of machine-building enterprises. A review of the literature has shown that a significant number of scientific papers today aim to present ways to increase the level of financial security, but not all of them understand that the most significant need of the subjects of financial support of machine-building enterprises is the assessment and consideration of negative factors in decision-making. Thus, the scientific task was to present a theoretical and methodological approach to assessing the negative factors of martial law on the management of financial security of a modern Ukrainian enterprise in the field of mechanical engineering. The problem was solved using the methods of system and hierarchical analysis, graph theory and on the basis of expert assessments. As a result, we presented step-by-step instructions on how to assess the negative factors and identified the most significant of them, the consideration of which is critically important in the process of managing the financial security of a modern Ukrainian machine-building enterprise. Prospects for further research will be based on expanding this list and narrowing the focus on the internal processes of machine-building enterprises.

Application of Big Data and Quantum Computing in the Secure Federated Internet of Things

Federated Internet of Things (IoT) presents both unprecedented opportunities and challenges in security and data management. This study explores the integration of big data analytics and Quantum Computing as potential solutions to address security concerns within the Federated IoT ecosystem. The study examines the implications of leveraging big data analytics to process and analyze the massive volume of data generated by IoT devices. Advanced analytics techniques, including machine learning and anomaly detection algorithms, are employed to enhance the detection and mitigation of security threats such as unauthorized access, data breaches and malicious attacks. Furthermore, the study investigates the role of Quantum Computing infrastructure in providing scalable and reliable resources for securely storing, processing and transmitting IoT data. By offloading computational tasks to quantum-based platforms, the aim is to alleviate the burden on edge devices while ensuring robust security measures are in place to safeguard sensitive information. A comprehensive review of existing literature and case studies identifies key challenges and opportunities in implementing big data and Quantum Computing solutions within the Federated IoT environment. The study also proposes potential frameworks and methodologies for integrating these technologies effectively, considering factors such as data privacy, scalability and interoperability. Overall, this research aims to advance secure IoT systems by leveraging big data analytics and cloud computing. By addressing security concerns proactively and adopting innovative approaches, the goal is to create a more resilient and trustworthy Federated IoT ecosystem, benefiting society at large.

Network Functions in Cloud: Kubernetes Deployment Challenges

The rapid evolution of cloud computing has paved the way for deploying network functions (NFs) in cloud environments, significantly enhancing the flexibility, scalability, and efficiency of modern network infrastructures. Kubernetes, an open-source container orchestration platform, has emerged as a leading tool for deploying and managing these cloud-based network functions. However, despite its widespread adoption, Kubernetes presents several deployment challenges specific to network functions, stemming from its design, scalability, and operational intricacies. This paper delves into the core challenges faced during the deployment of network functions on Kubernetes, focusing on issues related to network performance, security, service orchestration, and resource management. The abstract aims to provide an overview of the technical hurdles and propose potential strategies to overcome them, thus contributing to the optimization of Kubernetes-based NF deployments in cloud environments. By analyzing existing literature and case studies, the paper identifies key areas where improvements are needed and discusses the implications of these challenges for the future of cloud-based network functions. Ultimately, the paper seeks to guide network architects and cloud engineers in better understanding the complexities of Kubernetes deployments for network functions and in developing more effective strategies for successful implementation.

The role of community policing in the Addis Ababa City Administration's efforts to maintain community safety and security

This study aimed to assess the role of community policing in maintaining community safety and security in the Addis Ababa city administration and to assess the role of community policing in maintaining community safety and security community policing initiatives, information sharing, and attitudes toward community policing among community members on local security management practices. A descriptive case study was adopted, in which the target population of 328 respondents was stratified randomly into three sub-cities: Arada, Kirkos, and Addis Ketema. A structure questionnaire was used as the research instrument. Quantitative data were analyzed using descriptive and inferential statistics. The findings revealed that the role of community polices included paper initiatives among members, information sharing on community policing, and community policing among community members because there were no local security management practices affecting security practices. It was recommended that the community be made aware of their role in community policing and that the police should ensure that they interact well with the community so that the members of the community will willingly provide information to the police, which may help prevent crime.

Enhancing security and risk management with predictive analytics: A proactive approach

and interconnected world. This paper explores the transformative potential of predictive analytics in enhancing security and risk management by shifting from reactive to proactive strategies. Examining the theoretical foundations and application areas of predictive analytics, the paper highlights how organizations can anticipate and mitigate risks across various domains, including cybersecurity, fraud detection, and supply chain management. The benefits of predictive analytics, such as early threat detection and optimized resource allocation, are discussed alongside data quality, privacy, and model interpretability challenges. Additionally, emerging trends, such as artificial intelligence, real-time data analytics, and blockchain technology, are key drivers shaping the future of predictive analytics in risk management. The paper concludes by emphasizing organizations' need to integrate predictive analytics into their risk management frameworks to enhance resilience and ensure long-term success in a rapidly evolving risk landscape.

Effect of Emergency Preparedness on Security in Kenyan Universities: A Case Study of Garissa University

Security emergencies and threats have lately been on the increase, with public universities in Kenya reporting many incidents of insecurity. Despite investigation efforts by university management, the high prevalence of insecurity has continued, pointing to the need to determine the relationship between security emergency preparedness and security management in public universities in Kenya. This paper explores the existing types of emergency situations, the existing types of emergency response approaches, and the level of institutional emergency preparedness and their influence on security at Garissa University. Utilizing the emergency Management Theory, the paper points out that the common type of emergency situation in the study institution is terror-related attacks. Training is the common emergency situation response strategy that the university is utilizing in readiness to face emergency situations, and strict checking at the gate is the most common plan that the university has used to deal with emergency situations within its compound and emergency planning in the study institution has enhanced security through control of movement. The study recommends that the university invest in modern and advanced information technology infrastructure and systems to avert incidences of insecurity.

Active Security Surveillance and Object Detection

Active security surveillance and object detection have become crucial components in modern security systems, driven by advancements in computer vision and machine learning. These systems are designed to enhance real-time monitoring and threat detection across various environments, such as public spaces, transportation hubs, and critical infrastructure. Active security surveillance systems continuously scan and analyze video feeds or sensor data to identify potential security threats, unauthorized activities, or anomalies. They employ advanced algorithms for motion detection, facial recognition, and behavioural analysis, allowing for the rapid identification of suspicious activities or individuals. Object detection, a key element of these systems, involves the identification and classification of objects within a given scene. This is typically achieved through deep learning techniques, such as convolutional neural networks (CNNs), which can accurately detect and categorize objects like vehicles, weapons, or unattended bags. The integration of object detection with active surveillance enables more precise and automated responses to potential threats, reducing reliance on human operators and minimizing the risk of oversight. The adoption of active surveillance and object detection technologies has led to significant improvements in security management, offering enhanced situational awareness and faster response times. However, these technologies also raise concerns regarding privacy, data security, and ethical implications, particularly in their deployment in public and private spaces. Addressing these concerns requires careful consideration of data governance, transparency, and regulatory compliance to balance security needs with individual rights. As technology continues to evolve, ongoing research and development are essential to improving the accuracy, efficiency, and ethical deployment of active security surveillance and object detection systems.

Improved Winter Wheat Yield Estimation by Combining Remote Sensing Data, Machine Learning, and Phenological Metrics

Accurate yield prediction is essential for global food security and effective agricultural management. Traditional empirical statistical models and crop models face significant limitations, including high computational demands and dependency on high-resolution soil and daily weather data, that restrict their scalability across different temporal and spatial scales. Moreover, the lack of sufficient observational data further hinders the broad application of these methods. In this study, building on the SCYM method, we propose an integrated framework that combines crop models and machine learning techniques to optimize crop yield modeling methods and the selection of vegetation indices. We evaluated three commonly used vegetation indices and three widely applied ML techniques. Additionally, we assessed the impact of combining meteorological and phenological variables on yield estimation accuracy. The results indicated that the green chlorophyll vegetation index (GCVI) outperformed the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) in linear models, achieving an R2 of 0.31 and an RMSE of 396 kg/ha. Non-linear ML methods, particularly LightGBM, demonstrated superior performance, with an R2 of 0.42 and RMSE of 365 kg/ha for GCVI. The combination of GCVI with meteorological and phenological data provided the best results, with an R2 of 0.60 and an RMSE of 295 kg/ha. Our proposed framework significantly enhances the accuracy and efficiency of winter wheat yield estimation, supporting more effective agricultural management and policymaking.

Enhancing Cybersecurity with Blockchain-based Identity Management

In the digital age, the rapid increase in cyber threats has highlighted the need for more robust and secure identity management systems. Traditional centralized identity management systems are often vulnerable to data breaches, unauthorized access, and identity theft, posing significant risks to individuals and organizations. This paper explores the potential of blockchain technology to enhance cybersecurity through decentralized identity management. By leveraging the inherent properties of blockchain—such as immutability, transparency, and decentralization—blockchain-based identity management systems offer a more secure and efficient alternative to traditional methods. The paper provides an in-depth analysis of how blockchain can be utilized to store and manage digital identities securely, reducing the risk of identity fraud and data breaches. It examines various blockchain-based identity management frameworks, highlighting their advantages and potential challenges in implementation. Furthermore, the study discusses the integration of blockchain with other emerging technologies, such as cryptographic algorithms and smart contracts, to create a more resilient cybersecurity infrastructure. Through a review of current literature and case studies, the paper demonstrates the effectiveness of blockchain in enhancing data security and privacy while promoting user control over personal information. The findings suggest that adopting blockchain-based identity management could significantly mitigate cybersecurity threats and enhance trust in digital transactions. However, the paper also acknowledges the need for addressing scalability, regulatory compliance, and interoperability issues to fully realize the potential of blockchain in cybersecurity. This research contributes to the growing body of knowledge on innovative cybersecurity solutions and provides a foundation for future studies in the field.

Ризики безпекового управління суб’єктів логістичного діяльності у фокусі сучасних викликів: практичний інструментарій

In today’s conditions of operation of subjects of logistics activity without risk – there is no such thing. It should be noted that the risk must be calculated to the maximum permissible limit. From a practical point of view, market assessments of risk are varied. Yes, it is essential for business entities not to be afraid of any mistakes in their market activity since no one is immune from them, and most importantly, it is necessary to constantly adjust the system of actions from the standpoint of improving the logistics system. The article examines the risks of security management of logistics entities. It was noted that the main functions of risk are innovative, regulatory, protective, compensatory, socio-economic, and analytical. It has been proven that indicators for quantitatively assessing risks in the security system at logistics entities cover absolute values (dispersion, root mean square deviation, semi-variation, semi-quadratic deviation) and relative values (probability, coefficient of variation, risk coefficient). The main methods of regulating the degree of risk of logistics activity subjects are highlighted: risk avoidance, risk compensation, prognostication, monitoring, risk retention, creation of special reserve funds, involvement of external sources, transfer of risk, hedging, and risk reduction. Based on this, it has been proven that the goal of reducing the risks in security management of logistics entities can be considered a comprehensive countermeasure against potential and real threats, as well as ensuring a high development potential of the studied structures. It should be noted that the goal of reducing the risks of security management of logistics entities can be considered comprehensive countermeasures against potential and actual threats, the elimination and minimization of which guarantee the business entity stable and maximally effective functioning in the changing conditions of the external and internal environments at the current moment in time, and as well as ensuring the high potential of development of the subjects of logistics activity in the future. Keywords: logistics, security, security system, safety-oriented management, logistics management, subjects of logistics activity, security management risks.

A Structured Lightweight Encryption Architecture for Data Protection in IoT

Accelerated growth in networking and semiconductor devices led to the increase of Internet-connected devices over recent years. The applications of IoT provide pervasive admittance, scalability, and distribution of data using the cloud for the storage and processing of data. All the actors involved in the grid affect the trust. Managing security and deploying it throughout the IoT environment is required to audit and protect sensitive data. Security management involves authentication to avoid unauthorized entry and encryption at transmission. Though there are standard methodologies employed in data protection in the cloud and network layers, there is a requirement for solutions with low foot-prints suitable for constrained IoT devices. While there are existing lightweight cipher designs performing well with less memory footprint, the performance in the cloud is less compared to traditional cipher techniques. So, a balanced solution with less memory footprint and resistance against various attacks in the cloud environment is required. A lightweight cipher design is proposed to enable secure communication among devices through the cloud, which is combined with Elliptic Light (ELLI) and an Authentication Function (AF) is proposed using RC4 to ensure integrity and authentication and exchange secret key. The cipher design proposed provides increased efficiency and fixes the weakness in standard lightweight algorithms. This algorithm is simple and efficient, with 60% less memory utilization and 53% less execution time than other lightweight solutions. The stability is high in linear and differential cryptanalysis and resistive against DDoS attacks.

GESTÃO DE SEGURANÇA PÚBLICA: DESAFIOS E PERSPECTIVAS PARA A INTEGRAÇÃO DAS FORÇAS DE SEGURANÇA

Public security management is a complex challenge that requires efficient coordination between various security forces. Integration between these forces is essential to ensure an effective response to crimes, disasters and emergencies. The general objective of this study is to demonstrate the importance of integrating public security forces, analyzing their practices, challenges and benefits. Initially, research was carried out on public security management, which demonstrated that prevention is a more effective means than reprimand in security management. And this path led to highlighting the importance of the integration of security forces, proving to be essential for a coordinated approach to public security. To understand the integration of security forces, international models were analyzed, after which the possibility, need and challenges of this integration in Brazil were analyzed. The analysis is supported by a review of relevant literature and contemporary management models.

Fine-Resolution Wetland Mapping in the Yellow River Basin Using Sentinel-1/2 Data via Zoning-Based Random Forest with Remote Sensing Feature Preferences

Accurate wetland classification in the Yellow River Basin (YRB) is crucial for China’s ecological security, sustainable development, and wetland resource management. This calls for more sustained research on regional variations and studies on remote sensing features, especially with temporal considerations. To address this and the optimization of feature extraction as well as ranking, Sentinel-1 and Sentinel-2 images were used. Additionally, to achieve more precise wetland classification, the YRB was subdivided into four regions for random forest classification. The results show that different remote sensing indices effectively distinguish various wetland types and that key percentiles play a significant role in distinguishing wetland types. The 10 m refined wetland classification map for 2020, with an overall accuracy of 85%, demonstrates that this framework can meet the needs of conventional large-scale wetland analysis and management. The total area of wetlands in the YRB in 2020 was 33,554.67 km2, mainly distributed in the upper reaches of the YRB (71%), with seasonal marshes being predominant. The total water area reached 8538.64 km2, primarily distributed in the upper reaches of the YRB (57.40%). This high-resolution wetland map offers crucial insights and tools for monitoring, protecting wetland resources, and shaping policies, advancing regional sustainable development goals.

Deciphering pathogens inactivation mechanism during anaerobic co-digestion of food waste and waste activated sludge: The role of pH

Anaerobic co-digestion of waste activated sludge (WAS) with food waste (FW) represents a viable and effective approach to energy recovery and pollutant elimination. Little is known about the effect of alternating change of acid/alkaline conditions on pathogens inactivation within the co-digestion system of WAS and FW. In this study, pH changes during batch anaerobic co-digestion with WAS/FW ratios varying from 0.4 to 8, and their impact on pathogen inactivation were investigated using Escherichia coli as a model pathogen. At WAS/FW=1 and 2, serious acidification (pH<5.5) was effectively mitigated, and the pH transitioned from 6.4 during the acidogenesis stage to >8.5 during the methanogenesis stage. WAS/FW=1 achieved the highest methane yield (256.7 mL/g VS), which was 38.9 % greater than that in their mono-digestion scenario. A 2.37 − 3.87 log10 reduction in E. coli abundance occurred in all tested conditions during the acidogenesis stage, and a further reduction to an undetectable level was achieved only in WAS/FW reaching 1 subsequently. Low pH induced free volatile fatty acids dominated E. coli inactivation during the acidogenesis stage. During the methanogenesis stage, the combined action of OH− and the resultant free ammonia contributed to E. coli inactivation with free ammonia being more efficient in its action. This research contributes to the advancement of sustainable and secure management of organic wastes.