Security System Research Articles

From public to market use: a historical perspective on the changes in health data collection and use in France from 1930 to the present

Purpose This study aims to understand the progressive marketization of health data collection and use, through a study of its historical development in France, from the 1930s to the present day. Design/methodology/approach The authors collected a set of legal, institutional, political and media data. These came from the INA (National Audiovisual Institute), the French national newspaper websites and the websites legifrance.gouv.fr and vie-publique.fr. The authors then conducted a thematic content analysis. Findings The study results highlight the changes in the health-care system related to the increased use of data in France over three major periods. The first period – 1930s to 1980s – is marked by the creation of the French social security system to collect large sets of data to better manager people’s health care. The second period – 1980s to 2000s – is characterized by the adoption and assimilation of tools to manage patient data through several national and European regulations. The last period – 2000s to the present – saw the introduction of measures in favor of the digitalization of health care, and consequently of data, in parallel with the advancement of digital technologies in general. The institutional dynamics in healthcare have evolved with the nature of the actors and their practices, in connection with new perceptions about health data. Originality/value This research sheds light on the historical transformation of health data collection and use in France, revealing the involvement of diverse stakeholders, the discourses driving data development and the need for regulation. It exposes the dual nature of health data collection and use, initially sanctioned by the state and public entities but later exploited for private interests.

The construction of sports tourism projects under the strategy of national fitness by wireless sensor network

Abstract In recent years, sports events have continuously attracted people’s attention, and people’s enthusiasm and participation in sports have been rising. The sports + tourism model can meet people’s travel needs and enjoy sports-related entertainment and experiences. With the expansion of sports tourism, people have a new understanding of sports + tourism. Sports tourism based on the current strategic background of national fitness is an emerging industry, and it has had a certain degree of development prospects before this. This article explores traditional sports tourism with national characteristics under the blessing of the wireless sensor network. This exploration discusses and explores the development and construction of local sports + tourism in terms of literature and logical analysis. The experimental results of this article show that most consumers are not satisfied with the construction of sports tourism network information, and they are the least satisfied with key information coverage, e-commerce, and security systems. It shows that in these aspects, the construction of sports tourism informatization is relatively lagging, and it is in urgent need of improvement and improvement.

Assessing the equilibrium of food supply and demand in China’s food security framework: a comprehensive evaluation, 1980–2017

The continuous upward trajectory observed in total grain production serves as a fundamental pillar for guaranteeing food security. Nevertheless, relying solely on the measurement of China’s overall food security status through total grain output is inherently biased and neglects to capture the comprehensive nature of food security. This study adopts a food supply and demand balance perspective and constructs an evaluation indicator system for food security based on indicators such as grain yield per unit area, per capita grain possession, grain inventory, and inventory ratio. The weight of each indicator in the food security system is calculated using the entropy value method, and a comprehensive evaluation of China’s food security level from 1980 to 2017 is conducted. The study revealed that China’s food supply and demand exhibited a discernible upward trajectory in development. Notably, the food supply demonstrated greater volatility, whereas the food demand remained relatively stable but experienced incremental growth. Between approximately 1985 and 1993, China’s food supply and demand subsystem briefly experienced a state of mild imbalance, followed by a state of moderate imbalance around 2003. These imbalances were primarily attributed to insufficient effective food supply. In terms of the equilibrium between supply and demand in the context of food security, China’s food supply and demand exhibit a predominantly balanced condition with a slight surplus, wherein the adequacy of food supply significantly influences food security. Furthermore, the provision of policy support serves as a robust assurance for food security, and China’s existing policy framework for food security demonstrates a constructive impact.

Comprehensive framework for implementing blockchain-enabled federated learning and full homomorphic encryption for chatbot security system

Comprehensive framework for implementing blockchain-enabled federated learning and full homomorphic encryption for chatbot security system

Human information security in the system of national security: challenges and threats

Human information security in the system of national security: challenges and threats

Speed Breeding in Cereal Crops: Accelerating Genetic Improvement for Rapid Agricultural Advancement

The global demand for food is rising due to population growth, climate change, and evolving consumer preferences. Traditional plant breeding programs typically require a decade or more to develop and release new crop varieties. Speed breeding is a cutting-edge technique designed to expedite genetic improvement by significantly reducing the seed-to-seed cycle. This review explores the principles, applications, and potential of this method in accelerating agricultural advancement. Originating from NASA's experiments for food production in space, the approach has evolved into a highly efficient strategy for speeding up breeding cycles in cereal crops. By manipulating light, photoperiodic regimes, temperature, and humidity within controlled environments, it is possible to achieve up to six generations of photo-insensitive crops and two to three generations of other crops per year. This rapid generation turnover provides a unique opportunity for accelerated genetic mapping, trait stacking, and enhanced genomic selection. Speed breeding complements modern breeding technologies such as genome editing, high-throughput genotyping, and CRISPR, facilitating quicker development of crop varieties with improved traits. Its applications extend to boosting transgenic pipelines and understanding critical physiological traits in crops. Furthermore, it aligns with breeding methods like single plant selection and single seed descent, offering a more efficient path to desirable outcomes. However, challenges remain, including the need for specialized infrastructure, the impact of genotypic variations, and potential stress responses due to accelerated conditions. Despite these hurdles, speed breeding represents a promising tool in the quest for food security and resilient agricultural systems. This review examines its potential to transform plant breeding, reduce cultivar development times, and contribute to rapid agricultural advancement in a changing world.

Novel intrusion detection system based on a downsized kernel method for cybersecurity in smart agriculture

Smart farming aims to ameliorate the quantity and quality of farms products using modern information and communication technologies. Agriculture has always used cutting-edge technology to improve profitability, efficiency, and safety as well as creating the opportunity for more environmentally friendly operations. In smart agriculture network communication between smart devices is necessary to managing the agriculture process. Consequently, the possibility of information being exposed to hacking is very likely, which makes the information security system extremely important and cannot be neglected. Even though many solutions have been developed to overcome cyberattacks in smart networks, some limitations are observed, such as solving multi-class problems, high feature dimensions, and overhead computation. To address the aforementioned issues, an intelligent Intrusion Detection System (IDS) for identifying cyberattacks in the Internet of Agriculture Things (IoAT) is proposed. The developed model uses a proposed reduced kernel method, the Downsized Kernel Partial Least Square (DKPLS) to extract and reduce data feature dimension to improve detection performance. The DRKPLS approach is used to reduce the dimension of a kernel matrix generated using the Kernel Partial Least Square (KPLS) technique by selecting the most important features. In classification phase, the Kernel Extreme Learning Machine (KELM) is used to classify data for binary and multi-class classification. The proposed IDS is evaluated on a new Industrial IoT Dataset, X-IIoTID. The developed approach achieved higher performances for binary classification and for multi–class classification compared to others machine learning and deep learning approaches. The proposed approach achieved an accuracy rate of 99.92% for binary classification and 99.99% for multi–class classification. Furthermore, the higher percentage in multi-class classification is obtained for the two types of attacks Ransom Denial of Service (RDoS) and Command_Control respectively.

The Research of Collaborative System of Remote Sensing Monitoring Based on Bimodal Cloud

Abstract. Cloud service is based on cloud computing, Offering a On-Demand service to every terminal equipment of computing resource pool. This paper designed and developed a coordinated operating system based on bimodal cloud. This system is taken mutual scheduling mechanism into account, which is capable of storing massive amounts of heterogeneous remote sensing data and provides fast indexing of data based on various characteristics, integrated Satellite transit forecast, DOM Produce, coordinated change information extraction and results sharing based on Nginx load balancing, in addition, the system designed two layer security system to ensure the safety of data results.The "YunYao" geographic information service rendering engine built on the dual-state cloud platform significantly outperforms mainstream platforms in the same testing environment. Its rendering speed surpasses ArcGIS Desktop by more than two times, exceeds GeoServer by more than four times, and is over seven times faster than ArcGIS Server. Remote sensing practitioners can quickly and conveniently utilize this system, while providing convenient functionalities that enable remote sensing scientists to independently conduct scientific research and development using this system. Experimentation and practice shows that this system simplified routine work flow, improved work efficiency, has a important reference meaning to remote sensing monitoring.

Virtual Trailroom using Machine Learning

The virtual trail room system receives a real-time video feed from a camera and processes data with the OpenCV computer vision library and Haarcascade classifier. In order to correctly identify faces in the video feed, the Haarcascade classifier is trained on a sizable dataset of human faces.The virtual room system may function independently, which eliminates the need for additional employees and associated expenditures, in contrast to typical security systems that demand the presence of physical security officers.

Network security AIOps for online stream data monitoring

AbstractIn cybersecurity, live production data for predictive analysis pose a significant challenge due to the inherently secure nature of the domain. Although there are publicly available, synthesized, and artificially generated datasets, authentic scenarios are rarely encountered. For anomaly-based detection, the dynamic definition of thresholds has gained importance and attention in detecting abnormalities and preventing malicious activities. Unlike conventional threshold-based methods, deep learning data modeling provides a more nuanced perspective on network monitoring. This enables security systems to continually refine and adapt to the evolving situation in streaming data online, which is also our goal. Furthermore, our work in this paper contributes significantly to AIOps research, particularly through the deployment of our intelligent module that cooperates within a monitoring system in production. Our work addresses a crucial gap in the security research landscape toward more practical and effective secure strategies.

Sistem Keamanan Kendaraan Jarak Jauh Menggunakan GPS Berbasis NODEMCU ESP8266

The use of motorcycles as a means of personal transportation is a practical and easy-to-use transportation option in various crowded road conditions. In general, vehicle security systems are still very weak, and additional security systems are needed to prevent theft, especially in older vehicles. To increase security on motorbikes, researchers developed a security system that can monitor the location of motorbikes that can be monitored and controlled via an Android application. In making this prototype, the NodeMCU ESP8266 microcontroller as the Internet of Things (IoT), Ublox Neo-6M GPS module, and relays to control motorcycle electricity. The GPS module is used to determine the location based on the latitude and longitude of the motorcycle directly and will be sent to the server and then displayed through the Android application. In addition to displaying location coordinates, this application can also control motorcycle electricity with NodeMCU ESP8266 to activate relays connected to motorcycle electricity to function as turning on or off the motorcycle engine and there is a location history menu obtained from the GPS module.

Biosensor-Driven IoT Wearables for Accurate Body Motion Tracking and Localization

The domain of human locomotion identification through smartphone sensors is witnessing rapid expansion within the realm of research. This domain boasts significant potential across various sectors, including healthcare, sports, security systems, home automation, and real-time location tracking. Despite the considerable volume of existing research, the greater portion of it has primarily concentrated on locomotion activities. Comparatively less emphasis has been placed on the recognition of human localization patterns. In the current study, we introduce a system by facilitating the recognition of both human physical and location-based patterns. This system utilizes the capabilities of smartphone sensors to achieve its objectives. Our goal is to develop a system that can accurately identify different human physical and localization activities, such as walking, running, jumping, indoor, and outdoor activities. To achieve this, we perform preprocessing on the raw sensor data using a Butterworth filter for inertial sensors and a Median Filter for Global Positioning System (GPS) and then applying Hamming windowing techniques to segment the filtered data. We then extract features from the raw inertial and GPS sensors and select relevant features using the variance threshold feature selection method. The extrasensory dataset exhibits an imbalanced number of samples for certain activities. To address this issue, the permutation-based data augmentation technique is employed. The augmented features are optimized using the Yeo–Johnson power transformation algorithm before being sent to a multi-layer perceptron for classification. We evaluate our system using the K-fold cross-validation technique. The datasets used in this study are the Extrasensory and Sussex Huawei Locomotion (SHL), which contain both physical and localization activities. Our experiments demonstrate that our system achieves high accuracy with 96% and 94% over Extrasensory and SHL in physical activities and 94% and 91% over Extrasensory and SHL in the location-based activities, outperforming previous state-of-the-art methods in recognizing both types of activities.

Blockchain-Based Zero-Trust Supply Chain Security Integrated with Deep Reinforcement Learning for Inventory Optimization

Modern supply chain systems face significant challenges, including lack of transparency, inefficient inventory management, and vulnerability to disruptions and security threats. Traditional optimization methods often struggle to adapt to the complex and dynamic nature of these systems. This paper presents a novel blockchain-based zero-trust supply chain security framework integrated with deep reinforcement learning (SAC-rainbow) to address these challenges. The SAC-rainbow framework leverages the Soft Actor–Critic (SAC) algorithm with prioritized experience replay for inventory optimization and a blockchain-based zero-trust mechanism for secure supply chain management. The SAC-rainbow algorithm learns adaptive policies under demand uncertainty, while the blockchain architecture ensures secure, transparent, and traceable record-keeping and automated execution of supply chain transactions. An experiment using real-world supply chain data demonstrated the superior performance of the proposed framework in terms of reward maximization, inventory stability, and security metrics. The SAC-rainbow framework offers a promising solution for addressing the challenges of modern supply chains by leveraging blockchain, deep reinforcement learning, and zero-trust security principles. This research paves the way for developing secure, transparent, and efficient supply chain management systems in the face of growing complexity and security risks.

‘Peace-Loving Countries, Unite!’ British Reception of Soviet Declarations on ‘Collective Security’ at the Turn of 1934

Soviet-German relations were very conducive for both countries after the Rapallo Treaty was concluded in 1922. There were some signs of difficulties during the Great Depression but they did not influence the willingness of Berlin and Moscow to cooperate. After Adolf Hitler came to power, however, the deterioration of bilateral relations of the two countries became apparent. Finally, at the turn of 1934, a wave of official Soviet declarations on starting cooperation with the ‘peace-loving’ capitalist countries against the aggressor within the framework of the collective security system took place. The article’s aim is to explain whether or not the British Cabinet took notice of these speeches and what attention it paid them. It is important to determine how the British diplomats interpreted these declarations and what was the justification for their viewpoints. Previously, London had tried to separate Germany from the USSR. Hence, the question as to the British government’s understanding of the new situation arised as do the potential steps taken on the international scene once German-Soviet cooperation ceased to exist at the end of 1933. The deterioration of Soviet-German relations had the potential to influence British diplomacy, which was devoted to the idea of collective security.

Online Assessment in the Spotlight: A Case Study in a Spanish University

Objective. This exploratory case study aims to analyze the attitudes, practices and preferences of higher education faculty and students in relation to online assessment. Methodology. A quantitative methodology based on a questionnaire was used, and a descriptive and correlational analysis was conducted. Participants included students and faculty members from a Spanish on-site university. Results. Certain similarities and differences in attitudes, practices and preferences between students and teachers are presented. Some relationships, albeit weak, were also identified between the online assessment practices used and some characteristics of the teachers’ profile or teaching (teaching experience, teaching load and class size). Conclusions. The findings show that a large part of the teaching staff shows some reluctance in the design and implementation of online assessment strategies beyond traditional exams, as well as a great concern about student control and the danger of fraud. On the other hand, students show generally more positive attitudes towards online assessment than the teaching staff. The recommendations point to the consideration of the role of institutional support and teacher training units for the improvement of teachers’ digital competence in relation to online assessment, the importance of updating security systems in technical infrastructures, and adjusting the effects of teaching load and class size when online assessment is applied.

Security augmenting of optical cryptosystem based on linear canonical transform domain using a full phase encoding technique

For the purpose of alleviating the vulnerability of double random phase encryption system in the linear canonical transform domain, a novel approach for optical security and cryptographic systems is presented. This proposed system uses a fully phase encoding technique to augment the security of encryption system in the linear canonical transform. The first step in this system involves phase encoding of the initial amplitude image to be ciphered and then modulated by the phase masks. The decryption process of image is the reversal operation of the encryption method. The effectiveness and sensitivity of our proposed cryptosystem for the encryption secret keys are verified. The resistance of our method against occlusion attacks is investigated. Moreover, the results demonstrate that the fully phase-based optical cryptosystem is more secure and robust than the amplitude-based scheme in a linear canonical domain.

A comprehensive investigation of clustering algorithms for User and Entity Behavior Analytics

IntroductionGovernment agencies are now encouraging industries to enhance their security systems to detect and respond proactively to cybersecurity incidents. Consequently, equipping with a security operation center that combines the analytical capabilities of human experts with systems based on Machine Learning (ML) plays a critical role. In this setting, Security Information and Event Management (SIEM) platforms can effectively handle network-related events to trigger cybersecurity alerts. Furthermore, a SIEM may include a User and Entity Behavior Analytics (UEBA) engine that examines the behavior of both users and devices, or entities, within a corporate network.MethodsIn recent literature, several contributions have employed ML algorithms for UEBA, especially those based on the unsupervised learning paradigm, because anomalous behaviors are usually not known in advance. However, to shorten the gap between research advances and practice, it is necessary to comprehensively analyze the effectiveness of these methodologies. This paper proposes a thorough investigation of traditional and emerging clustering algorithms for UEBA, considering multiple application contexts, i.e., different user-entity interaction scenarios.Results and discussionOur study involves three datasets sourced from the existing literature and fifteen clustering algorithms. Among the compared techniques, HDBSCAN and DenMune showed promising performance on the state-of-the-art CERT behavior-related dataset, producing groups with a density very close to the number of users.

Enhancing Cryptographic Primitives through Dynamic Cost Function Optimization in Heuristic Search

The efficiency of heuristic search algorithms is a critical factor in the realm of cryptographic primitive construction, particularly in the generation of highly nonlinear bijective permutations, known as substitution boxes (S-boxes). The vast search space of 256! (256 factorial) permutations for 8-bit sequences poses a significant challenge in isolating S-boxes with optimal nonlinearity, a crucial property for enhancing the resilience of symmetric ciphers against cryptanalytic attacks. Existing approaches to this problem suffer from high computational costs and limited success rates, necessitating the development of more efficient and effective methods. This study introduces a novel approach that addresses these limitations by dynamically adjusting the cost function parameters within the hill-climbing heuristic search algorithm. By incorporating principles from dynamic programming, our methodology leverages feedback from previous iterations to adaptively refine the search trajectory, leading to a significant reduction in the number of iterations required to converge on optimal solutions. Through extensive comparative analyses with state-of-the-art techniques, we demonstrate that our approach achieves a remarkable 100% success rate in locating 8-bit bijective S-boxes with maximal nonlinearity, while requiring only 50,000 iterations on average—a substantial improvement over existing methods. The proposed dynamic parameter adaptation mechanism not only enhances the computational efficiency of the search process, but also showcases the potential for interdisciplinary collaboration between the fields of heuristic optimization and cryptography. The practical implications of our findings are significant, as the ability to efficiently generate highly nonlinear S-boxes directly contributes to the development of more secure and robust symmetric encryption systems. Furthermore, the dynamic parameter adaptation concept introduced in this study opens up new avenues for future research in the broader context of heuristic optimization and its applications across various domains.

KYC using Smart Contract over Blockchain

Know Your Customer (KYC) is a requirement for financial institutions to use identity, qualification and risk to identify customers when establishing a banking relationship. With the focus on security, the KYC process has become difficult and costly to complete for any customer. In this article, we propose a convenient, instant, secure and transparent banking system KYC certification platform through Inter Planetary File System (IPFS) and block-chain technology. The system allows customers to open an account at any bank, complete the KYC process there, generate hashes using the IPFS network, and distribute them to other banks using the legacy urine of block-chain technology. Using a private key, all banks/financial institutions can use the IPFS network to securely store and store customer information (e.g. KYC) if the customer wants to open another account with that bank/financial institution. Additionally, leveraging block-chain technology ensures immutability and transparency of KYC information, thereby increasing security and reducing the risk of fraud or misuse of information. Storing hashed KYC data on the IPFS network ensures that important customer data remains transparent and accessible only to authorized users, thus maintaining confidentiality and compliance with data protection laws. In addition, the processes that this new system can easily adjust will increase efficiency in the banking sector, allowing companies to allocate resources more efficiently and focus on providing better customer service. By sharing KYC credentials across multiple banks, customers benefit from a quicker and faster account opening process; When banks come voluntarily, it builds trust and reduces the risks associated with new customers. Overall, the integration of block-chain and IPFS technology into the KYC process should transform the banking industry by providing efficient, secure and transparent solutions that enable customer satisfaction and compliance.

IOT Based Anti - Theft Flooring System Using NodeMCU (ESP8266)

Traditional anti-theft security systems often rely on easily circumvented methods such as motion sensors or wall-mounted devices, leaving vulnerabilities that can be exploited by savvy thieves. To address these limitations, we propose an innovative IoT-based anti-theft flooring system utilizing Node MCU technology. This system offers comprehensive monitoring of designated areas, detecting any movement within its coverage zone. Each secure flooring tile within the system is seamlessly integrated with IoT capabilities. Upon activation, the system begins monitoring the area, transmitting data over the internet to alert users of any intrusions. When an unauthorized individual steps onto the floor, sensors promptly detect the movement and relay the information to the central controller. The controller verifies the signal's validity and triggers the camera to capture images of the detected movement area. These images are then transmitted over the internet, allowing the owner to remotely assess the situation in real-time. By leveraging IoT technology, our anti-theft flooring system offers enhanced security measures, providing peace of mind for property owners. Keywords: IoT (Internet of Things), Anti-theft system, Flooring security, NodeMCU (ESP8266) ,IoT security , Real time monitoring.