Security System Research Articles

Enhancing Electronic Agriculture Data Security with a Blockchain-Based Search Method and E-Signatures

The production of digital signatures with blockchain constitutes a prerequisite for the security of electronic agriculture applications (EAA), such as the Internet of Things (IoT). To prevent irresponsibility within the blockchain, attackers regularly attempt to manipulate or intercept data stored or sent via EAA-IoT. Additionally, cybersecurity has not received much attention recently because IoT applications are still relatively new. As a result, the protection of EAAs against security threats remains insufficient. Moreover, the security protocols used in contemporary research are still insufficient to thwart a wide range of threats. For these security issues, first, this study proposes a security system to combine consortium blockchain blocks with Edwards25519 (Ed25519) signatures to stop block data tampering in the IoT. Second, the proposed study leverages an artificial bee colonizer (ABC) approach to preserve the unpredictable nature of Ed25519 signatures while identifying the optimal solution and optimizing various complex challenges. Advanced deep learning (ADL) technology is used as a model to track and evaluate objects in the optimizer system. We tested our system in terms of security measures and performance overhead. Tests conducted on the proposed system have shown that it can prevent the most destructive applications, such as obfuscation, selfish mining, block blocking, block ignoring, blind blocking, and heuristic attacks, and that our system fends off these attacks through the use of the test of the Scyther tool. Additionally, the system measures performance parameters, including a scalability of 99.56%, an entropy of 60.99 Mbps, and a network throughput rate of 200,000.0 m/s, which reflects the acceptability of the proposed system over existing security systems.

An efficient intrusion detection system for IoT security using CNN decision forest.

The adoption and integration of the Internet of Things (IoT) have become essential for the advancement of many industries, unlocking purposeful connections between objects. However, the surge in IoT adoption and integration has also made it a prime target for malicious attacks. Consequently, ensuring the security of IoT systems and ecosystems has emerged as a crucial research area. Notably, advancements in addressing these security threats include the implementation of intrusion detection systems (IDS), garnering considerable attention within the research community. In this study, and in aim to enhance network anomaly detection, we present a novel intrusion detection approach: the Deep Neural Decision Forest-based IDS (DNDF-IDS). The DNDF-IDS incorporates an improved decision forest model coupled with neural networks to achieve heightened accuracy (ACC). Employing four distinct feature selection methods separately, namely principal component analysis (PCA), LASSO regression (LR), SelectKBest, and Random Forest Feature Importance (RFFI), our objective is to streamline training and prediction processes, enhance overall performance, and identify the most correlated features. Evaluation of our model on three diverse datasets (NSL-KDD, CICIDS2017, and UNSW-NB15) reveals impressive ACC values ranging from 94.09% to 98.84%, depending on the dataset and the feature selection method. Notably, our model achieves a remarkable prediction time of 0.1 ms per record. Comparative analyses with other recent random forest and Convolutional Neural Networks (CNN) based models indicate that our DNDF-IDS performs similarly or even outperforms them in certain instances, particularly when utilizing the top 10 features. One key advantage of our novel model lies in its ability to make accurate predictions with only a few features, showcasing an efficient utilization of computational resources.

Politicised at Home but not in Council: The European Coordination of Social Security Systems

After seven years of negotiations, the European Parliament and the Council have yet to agree on the reform of one of the most essential regulations facilitating cross-border worker mobility in the EU: the Coordination of Social Security Systems (EC 883/2004). The lack of agreement remains puzzling, as all negotiating partners are generally in favour of the reform. Similar proposals on social policy, such as the Posting of Workers Directive or the Minimum-wage Directive, were also challenging to achieve but could be agreed upon. This article tests whether “unpolitics”—a destructive approach by populist governments in the Council to undermine EU policymaking—is the reason for the persisting deadlock on the file. The central finding of this article is that the interplay of populism and the status of member states as sending or receiving workers shape unpolitical behaviour. Contrary to expectations, unpolitics is largely absent in the behaviour of populist and non-populist governments. Unpolitical behaviour does not promise high gains for the populist governments from Central and Eastern Europe because these member states send workers. The smooth functioning of the freedom of workers is essential for them and their national discourses do not discuss the freedom of movement in the context of welfare chauvinism. Welfare chauvinism is much stronger in countries that receive workers, however, populists were not in power in these member states and therefore there was no unpolitical behaviour. The findings show that unpolitical behaviour is not used by mainstream governments, not even when it would seem likely from the nature of the policy issue. This article highlights that the probability of unpolitical behavior is influenced not only by the nature of the policy issue itself but also by domestic institutional and structural factors, as well as the national discourse.

Smart Agriculture-Based Food Quality Analysis with Healthcare Security System Using Cloud Machine Learning Model

Smart Agriculture-Based Food Quality Analysis with Healthcare Security System Using Cloud Machine Learning Model

The Future of Social Security in Emerging Economies: Challenges and Proposals

This article examines the future of social security in emerging economies, exploring the key challenges facing these countries and proposing potential solutions. Financial sustainability, universal coverage and equity are some of the main obstacles that must be overcome to ensure a robust and fair social security system. Through a literature review and case analysis, proposals are presented to improve the effectiveness and sustainability of these systems in the context of emerging economies.

Reflection of the Implementation of the Soviet Health System in the Complaints of the Inhabitants (1944–1953)

The subject of the article is health care. The development of a universal free health care system from the beginning of the Soviet occupation had to become, in conjunction with other social programmes, one of the cornerstones of the Sovietisation tools: on the one hand, these programmes made the whole population directly dependent on the benevolence of the state, on the other hand, it cannot be denied that free and “universally accessible” services helped the Soviet regime earn favour with many inhabitants. We could claim that industry based on industrialisation plans was the backbone of the Soviet regime, while the health care and social security systems were like the nervous systems branching out from the backbone and covering the whole of society. This article aims to examine how the implementation of the Soviet health care system in the first and most ruthless years of the occupation (1944–1953) looked from the bottom (how it was understood by the inhabitants and what problems they saw) and what this says about the Sovietisation process in this area. In order to examine this, the complaints of inhabitants sent to the Ministry of Health of the Lithuanian SSR will be studied, because it is from these complaints that we can learn not only about the control mechanism of the regime and the involvement of ordinary citizens in it, but also about some aspects of the daily life of ordinary residents.

Community-based elderly home care services policy and household consumption enhancement: Evidence from China

The influence of elderly consumption is increasing, and community-based elderly home care service (CEHCS), as an important social security system, will have a significant impact on household consumption levels and consumption structure. This paper utilizes data from five batches of CEHCS pilot policies implemented in China from 2016 to 2020, combined with data from the China Health and Retirement Longitudinal Study (CHARLS) from 2011 to 2020, to design a dynamic difference-in-differences model to systematically evaluate the dynamic effects and mechanisms of CEHCS on elderly household non-durable goods consumption. The study found that after the coverage of CEHCS, the consumption levels of non-durable goods such as clothing, daily necessities, and cultural and entertainment products in middle-aged and elderly households significantly increased year by year, while consumption of durable goods significantly decreased. Moreover, the impact of CEHCS was more pronounced in households with lower housing assets and rural households. Mechanism analysis suggests that CEHCS promote the increase in non-durable goods consumption by alleviating children's labor constraints, reducing the family's caregiving burden, and diminishing precautionary savings. This paper provides further empirical evidence on the pathways through which elderly care services influence household consumption behavior, which holds significant implications for proactively addressing population aging and unlocking household consumption potential through micro-institutional design.

Smart Home

The concept of a smart home integrates wireless electronic systems that connect household devices to a central interface, enabling remote control via smartphones or wall panels through Wi-Fi networks. This paper explores the core components of smart homes, including smart devices such as speakers, thermostats, lighting, and security systems, as well as the role of mobile applications in managing these devices. The paper discusses the various applications of smart homes, from home automation and security monitoring to energy management and health-focused initiatives. Additionally, it evaluates the pros and cons of smart home technology, highlighting benefits such as enhanced security, energy efficiency, and user convenience, while also addressing challenges related to cost, complexity, and cybersecurity risks. The paper concludes by emphasizing the transformative potential of smart homes in modern living, while recognizing the need for strategic approaches to mitigate associated challenges.

DEVELOPMENT AND IMPLEMENTATION OF AN INTEGRAL METHOD FOR ASSESSING THE LEVEL OF PROVISION OF GEOSPATIAL DEVELOPMENT OF TERRITORIAL COMMUNITIES

The study confirms the relevance and necessity of developing and implementing an integral method for assessing the level of provision of geospatial development of territorial communities. The research achieves the goal of developing and implementing an integral method for assessing the level of provision of geospatial development of territorial communities. It solves the following tasks: building a multi-level system of indicators for evaluating the level of provision of geospatial development of territorial communities; defining areas of developing and implementing an integral method for estimating the level of provision of geospatial development of territorial communities; determining the peculiarities of the application of the integral method for assessing the level of provision of geospatial development of territorial communities. An integral method for assessing the level of provision of geospatial development of territorial communities has been improved based on the application of analytical and expert methods, mathematical models, and a multi-level system of indicators, including functional, spatial, urban planning, environmental, regulatory, security, investment, stakeholder, infrastructural, social, and innovative signs and characteristics, which made it possible to form a quantitative basis for forecasting changes in the integral factor and decision-making in the field of ensuring the geospatial development of territorial communities. An improved multi-level system of indicators for assessing the level of provision of geospatial development of territorial communities, which includes local, systemic, group, and integral levels and such indicators as functional, spatial, urban planning, ecological, regulatory, security, investment, stakeholder, infrastructural, social, and innovative, that make it possible to take into account the signs, characteristics, and peculiarities of the functioning of territorial communities and regional development for developing and implementing an integral evaluation method. Keywords: geospatial development, territorial communities, land management, geographic information systems, land use, integral indicator.

Leveraging quantum uncertainty: Quantum randomness through the lens of classical communication networks

The generation of random numbers and the study of its properties have been an elusive field for a fair portion of the century. The application of random numbers is employed in many use cases such as cryptography, neural networks, numerical simulation, and gambling. The performance of each of these use cases is profoundly impacted by the employed random numbers; henceforth, the quality of randomness is of critical importance when it comes to their usage. A poorly generated random number can make a security system vulnerable, or any numerical or statistical evaluation misleading. Although various modes of classical random number generators exist and still function to provide strong cryptographic properties, emergence of quantum mechanical randomness has shed light on a novel path of generating certified randomness which supersedes the classical counterpart in terms of security. Harnessing quantum mechanical phenomena enables generation of true random numbers which can be certified and further implemented to elevate the net quality of the specific use cases. In this work, we generate and analyze random numbers from three different sources — 50: 50 beam splitter (BS), quantum key distribution (QKD) setup with classical post-processing scheme, and a commercially available quantum random number generator (QRNG) (ID Quantique (IDQ)). The quality of the generated random numbers from the various sources is checked in statistical tests and compared. Further on, we have developed a system which implements the QRNG-based random numbers to facilitate message authentication code (MAC) and one time password (OTP) protocols, demonstrating a communication network application. In this manner we discuss about a network which integrates quantum mechanics to the current classical networking approaches to enhance certain aspects of the networking protocol — in this case, the security.

Optimization of machine learning model training procedure on multi-gpu systems to enhance cyber security in telecommunication networks

This paper analyzes the optimization features of machine learning (ML) model training procedures using multi-GPU systems to enhance cyber security in telecommunication networks. A key aspect of the study is the use of data parallelism, which allows the distribution of the training load across multiple GPUs, significantly reducing training time and improving model accuracy-critical factors for rapid threat detection in cyberspace. A novel approach for optimizing data batch size using Mutual Information (MI) is proposed, which harmonizes the utilization of computational resources with the information content of the training data. MI helps to determine the optimal data batch size that minimizes training errors and improves model accuracy without a significant increase in training time. Experimental results demonstrate the substantial advantages of multi-GPU configurations compared to single-GPU setups, providing faster training and improved model accuracy. It was particularly emphasized that MI-guided batch size tuning significantly outperforms traditional manual tuning methods, ensuring higher validation accuracy and reducing training time. The study showed that the MI-based approach is an effective tool for addressing the problem of optimizing ML model training processes in real-world scenarios where cyber security is critical. The proposed methods allow ML models to train faster and more accurately identify potential threats, making them particularly relevant for telecommunication networks where a rapid response to new threats in real time is required. The implementation of modern computational technologies such as multi-GPU systems and MI-optimized training enhances the efficiency and accuracy of machine learning models. This, in turn, improves cyber security measures and ensures a more reliable defence of telecommunication networks against malicious attacks. It is noted that the proposed approaches can be adapted not only for cyber security but also for other areas where high model accuracy and fast training are important. Future research prospects include the development of new machine learning methods, particularly deep neural networks, the exploration of alternative computational architectures such as quantum computing or distributed systems, and their integration into real-time systems. Special attention should be paid to the ethical aspects of implementing automated cyber security systems, particularly in preventing bias in algorithms and ensuring fairness in their application.

Centralized vs. Decentralized Cloud Computing in Healthcare

Healthcare is one of the industries that seeks to deliver medical services to patients on time. One of the issues it currently grapples with is real-time patient data exchange between various healthcare organizations. This challenge was solved by both centralized and decentralized cloud computing architecture solutions. In this paper, we review the current state of these two cloud computing architectures in the health sector with regard to the effect on the efficiency of Health Information Exchange (HIE) systems. Our study seeks to determine the relevance of these cloud computing approaches in assisting healthcare facilities in the decision-making process to adopt HIE systems. This paper considers the system performance, patient data privacy, and cost and identifies research directions in each of the architectures. This study shows that there are some benefits in both cloud architectures, but there are also some drawbacks. The prominent characteristic of centralized cloud computing is that all data and information are stored together at one location, known as a single data center. This offers many services, such as integration, effectiveness, simplicity, and rapid information access. However, it entails providing data privacy and confidentiality aspects because it will face the hazard of a single point of failure. On the other hand, decentralized cloud computing is built to safeguard data privacy and security whereby data are distributed to several nodes as a way of forming mini-data centers. This increases the system’s ability to cope with a node failure. Thus, continuity and less latency are achieved. Nevertheless, it poses integration issues because managing data from several sites could be a problem, and the costs of operating several data centers are higher and complex. This paper also pays attention to the differences in aspects like efficiency, capacity, and cost. This paper assists healthcare organizations in determining the most suitable cloud architecture strategy for deploying secure and effective HIE systems.

How sustainable is tuna aquaculture? A methodology to assess the sustainability of seafood production systems

IntroductionConsidering the need to feed the increasing world population, it is essential to ensure that food production systems are sustainable. Aquaculture has been identified as a potential crucial source of sustainable food with its significant potential for future growth, notably by the recent EU regulations aiming at boosting food security and sustainable food systems. In this context, it is important to reflect on how the aquaculture of carnivorous species, which are exhibiting the highest growth rates, stands in respect of environmental impacts and contribution to the overall food supply. In particular, tuna aquaculture has been largely criticized for its high environmental impacts and general low sustainability.MethodsHere, we present a methodology to assess the sustainability of seafood production systems, with a first analysis applied to Atlantic bluefin tuna (BFT) aquaculture, detailed for both BFT fattening and farming.ResultsResults suggest that the limited economic and social contributions of BFT aquaculture do not currently compensate its environmental impacts with an estimate of a 3.4-fold higher long-term social cost than the short-term economic gain.DiscussionHowever, it is also a species where the production cycle is not closed in commercial operation, and with a significant potential for further improvement as the control of the production process increases.

A modified CNN-IDS model for enhancing the efficacy of intrusion detection system

As the security of computer networks in enterprises worldwide is dependent on the proper functioning of intrusion detection systems (IDSs) and intrusion prevention systems (IPSs), this effectiveness of both of them is of utmost priority. Leveraging diverse techniques, these network security systems are created to keep the reliability, the availability, and the integrity of the organizational networks safe. One plus point of using ML in intrusion detection system (IDS) is that it has successfully weeded out all the IDS attacks with a high degree of accuracy. In contrast, such systems may be believed to operate to their least competent levels when supersized data spaces have to be dealt with. In the process to solve this, application of feature selection techniques will play the crucial role to ignore non-relevant features which do not impact the issue of classification much. One more thing to keep in mind is that the ML-based IDSs often have problems with high false alarms and percentage accuracy because of the imbalanced training sets. The undertaking of this paper involves a through the analysis of the UNSW-NB15 intrusion detection data set as upon which our models will be tested and trained. We utilize two feature selection approaches: the PCA method, which is denoted as PCA, and the SVD method, called SVD. Furthermore, we categorize the datasets using these methods— Ridge Regression (RR), Stochastic Gradient Descent, and Convolutional Neural Network (CNN)-- on the transformed feature space. What is the most widely used for, is that it deals with both, binary and multiclass classification. The result measure that PCA and SVD are succeeded in getting better performance of IDS than others with enhancing the accuracy of classification models. More specifically, the RR classifier's precise was outstanding for the binary classification problem experiencing a rise in the accuracy from 98.13 % to 99.85 %. This shows the critical role of feature selection approaches and is also demonstrates the modeling capabilities of RR, SGD, and CNN classifiers and stands out as a solution to intrusion detection.

Partially funded social security and growth

AbstractThis paper investigates the relationship between economic growth and the degree of fundedness of a social security system in an overlapping generations model with family altruism. It is shown that the relationship between the degree of fundedness and economic growth is inverted U‐shaped so that a gradual increase in funding may harm growth if bequests are not operative within the family. Our findings put some caution on the conventional view that a higher degree of funded social security is beneficial for growth.

Public Health Security Systems Empowered by Artificial Intelligence for Early Monitoring and Prevention of Epidemics

Artificial intelligence (AI) methods have been extensively used for detecting and predicting Infectious Disease (ID) outbreaks as time series and modeling and evaluating Public Health responses. The significant tasks of PH monitoring and intervention present distinct technical difficulties, including limited data availability, absence of sufficient positive training examples, challenges in establishing benchmarks, measuring the effectiveness of management policies, complex relationships between spatial and time series elements, and more detailed risk assessments involving interaction and social networks. Conventional PH monitoring mainly depends on statistical methods. In recent years, there has been a significant expansion of approaches that AI enables. This research presents an AI method called Early Monitoring and Prevention of Epidemics (AI-EMPE) for enhancing PH security systems. The suggested approach converts a substantial amount of collected PH security incidents into separate incident characteristics and utilizes a Deep Learning (DL)--based detection technique to enhance EMPE. AI-EMPE incorporates sophisticated AI methods, including integrated Convolutional Neural Networks (CNN) and Backpropagation Neural Networks (BP-NN). These findings indicate that the integrated method is the most efficient in improving PH security systems.

Combating Counterfeit Products with Blockchain Technology to Enhance Trust and Authenticity in Consumer Markets

Objectives: Counterfeit products are becoming a global problem where consumers are fooled into buying fake goods without being able to verify their authenticity. By taking advantage of the secure and transparent nature of blockchain, this work aims to establish a system for consumers to verify the authenticity of products. Methods: Recently, blockchain has become more popular because it fosters trust between untrusted participants. A blockchain is a decentralized, distributed, and digital ledger that records transactions by means of blocks. It is a security technology that does not allow anyone to change or hack the block. Using blockchain technology, customers do not have to rely on third parties to confirm product originality. Findings: This work will show the effectiveness of blockchain. Blockchain creates a more secure and transparent system for product authentication. It helps in identifying counterfeit products entering the market and increases consumer confidence during purchase. The comparison of the transaction send rate and the average latency for each type of transaction is elaborated. The CPU consumption for each transaction type Add, Query, and Query-all are 1.81%, 1.11% and 1.27% respectively. The memory consumption by add, Query and Query all are respectively 53MB, 45MB and 92 MB. Novelty: Blockchain technology helps manufacturers to add real product serial numbers into the ledgers. The serial number stored in the ledger is protected from tampering. The stored serial number helps consumers check the authenticity of a product before buying it. To verify that a manufacturer or trader is genuine, consumers may also submit reviews of the products they purchase. The deeper integration of consumer reviews within the system offers a novel approach to enhancing trust and transparency in the verification process. We have also developed a user-friendly interface for manufacturers, sellers, and customers, enhancing the novelty of our solution. Keywords: Counterfeit product, Blockchain, Supply chain, Product Authenticity, Trust

Irregular IRS-aided SWIPT secure communication system with imperfect CSI

The performance of traditional regular Intelligent Reflecting Surface (IRS) improves as the number of IRS elements increases, but more reflecting elements lead to higher IRS power consumption and greater overhead of channel estimation. The Irregular Intelligent Reflecting Surface (IIRS) can enhance the performance of the IRS as well as boost the system performance when the number of reflecting elements is limited. However, due to the lack of radio frequency chain in IRS, it is challenging for the Base Station (BS) to gather perfect Channel State Information (CSI), especially in the presence of Eavesdroppers (Eves). Therefore, in this paper we investigate the minimum transmit power problem of IIRS-aided Simultaneous Wireless Information and Power Transfer (SWIPT) secure communication system with imperfect CSI of BS-IIRS-Eves links, which is subject to the rate outage probability constraints of the Eves, the minimum rate constraints of the Information Receivers (IRs), the energy harvesting constraints of the Energy Receivers (ERs), and the topology matrix constraints. Afterward, the formulated non-convex problem can be efficiently tackled by employing joint optimization algorithm combined with successive refinement method and adaptive topology design method. Simulation results demonstrate the effectiveness of the proposed scheme and the superiority of IIRS.

Study on the Legal Risks of Social Entities' Participation in Non-Emergency Transfers

Non-emergency transfer multi-level protection system is an important livelihood project in China and an important diversified support for a sound Chinese-style modern social security system, as well as an ideal governance tool to effectively solve the problems of non-emergency demand crowding out the emergency capacity, unbalanced transfer resources and differences in the quality of service, imperfect management mechanism and laws and regulations, and low social acceptance. We use structural theory to analyse the main problems in the current practice of China's non-emergency transfer security system, accordingly construct the non-emergency transfer security system, and put forward suggestions to promote the development of the system in a benign cycle. Improve the internal resource allocation mechanism to enhance the efficiency of non-emergency transfer services; focus on the cultivation of the talent team to steadily improve the quality of services; optimise the management system and strengthen the articulation mechanism to boost patients' beliefs in recovery; and strengthen the all-round management of information technology for market supervision to deepen the emotional communication and connection.

Exploring the environmental, economic, and social implications of metaverse adoption in business and management

Recently, Metaverse has received increasing attention from both the business and academic worlds. However, its actual use in business and management is still confusing and limited. This study aims to provide a comprehensive overview of the environmental, economic, and social implications of the adoption of this technology within organizations. In addition, enabling technologies to Metaverse and business contexts in which the platform can be applied are identified. First, this study applies a systematic literature review (SLR) to investigate the use of Metaverse in business and management. Secondly, a content and cluster analysis were applied to the final sample of 656 articles to identify the Metaverse's primary research areas and future research directions. The results reveal that Metaverse has mainly social and economic implications such as enhancing collaboration, increasing accessibility, facilitating training, and encouraging productivity and profitability for companies. The major concerns for its implementation are privacy, security, user health, inclusiveness in access, implementation costs, and lack of regulation. Moreover, the application of content and cluster analysis techniques intercepted eight primary research areas defined as user experience, optimization, research method, educational change, business opportunities, security systems, enabling technologies, and healthcare. The article provides valuable insights to support management decision-making when considering the adoption of this technology in specific business contexts. To the best of the authors' knowledge, this is one of the first SLRs on Metaverse in the business and management field that classifies the opportunities, barriers, complementary technologies, and business functions and processes in which the platform can potentially be applied.