Security System Research Articles

Hand gesture-based automatic door security system using squeeze and excitation residual networks

Viruses can be transmitted due to various aspects; one spreads through airborne droplets or the touch of multiple objects. This can occur in any area, including the entrance to the house or access to a room or deposit box. The spread of viruses that cause diseases like Covid-19 has caused many human casualties, and there is still the possibility of similar conditions appearing in the future. Several things need to be done to reduce the chances of spreading disease due to viruses, including developing contactless security support methods. This paper proposes a security system using hand gesture recognition using squeeze and excitation residual networks (SE-ResNet). This research offers a hand gesture recognition system for an automatic door system using SE-ResNet and the residual network (ResNet).

Blockchain and differential privacy-based data processing system for data security and privacy in urban computing

Recently, big data related to human movement, air quality, and meteorology have been generated in urban computing through sensing technology and the computing infrastructure. However, security problems arise as data utilization increases. If the sensing data from internet of things devices are constantly exposed, the users’ private information can be determined, a critical security risk that could result in privacy breaches. This paper proposes a secure data processing system using the blockchain and differential privacy for data security and privacy protection in urban computing. When a service provider requests information, the system generates it from urban computing data using machine learning. We apply differential privacy to these data to protect privacy. However, if a query repeats, differential privacy may provide insufficient privacy protection. Therefore, we reduce the total privacy cost by reusing noise for the same data and privacy parameters using the blockchain. Machine learning accuracy may decrease when noisy data are used for training. Thus, we increase accuracy by storing and appropriately using the model parameters generated by the same data in the blockchain. We design, simulate, and analyze the results of an experimental environment for reusing noise for differential privacy and parameter utilization of machine learning using the blockchain. The proposed approach reduces privacy costs compared to the existing mechanism while protecting data privacy. We demonstrate that, through parameter utilization, the accuracy improves compared to conventional mechanisms.

Information and analytical activity in the system of economic security of the enterprise

This article is devoted to understanding the importance of information and analytical support for management decisions in modern conditions, when there are threats of various nature. It has been proven that the selection of reliable necessary information, scientifically based methods of its analysis provide optimal solutions in these conditions, aimed at ensuring the effective operation of the enterprise. Analysis occupies an intermediate place between the selection of information and decision-making. It has been studied that depending on business conditions, different decisions are made, different techniques and methods of analysis are used. The main features that determine the content of analytical activity are highlighted, such as: the study of economic phenomena, objective assessment of the efficiency of the enterprise, scientific substantiation of business plans and identification of reserves at the enterprise. The main tasks of the information and analytical system of enterprises of network structures in modern conditions are highlighted. It is considered that the object of analysis is the economic activity of the enterprise, which can be divided into economic processes and financial results formed under the influence of objective and subjective factors.

Main directions of ensuring national security and mechanisms of public administration under martial law

The task of ensuring national security is currently a key one for Ukraine, as a condition and goal of reforms in all spheres of state and social activity. The content of transformations in this area should be linked to ensuring and comprehensively strengthening Ukraine's sovereignty and preserving its territorial integrity. The current situation in the world is characterized by the presence of “points of tension”, the desire of other states to implement their geopolitical plans and unresolved social problems. In addition, the state is the main institution that creates appropriate conditions for the functioning of each business entity. In Ukraine, there is a need to define the benchmarks of the security system, study international experience and implement it in the process of improving national security legislation. The article examines and analyzes the theoretical foundations of the state management of national security development, reveals the essence of such concepts as “national security”, “economic security”; identifies the components of national and economic security of Ukraine; mechanisms of state management of economic security; characterizes three main types of security threats, namely: external and internal; real and potential; global, regional and local; identifies threats affecting economic security; develops recommendations for improving the effectiveness of the state management of economic security.

Research on the Spatio-temporal Coupling Relationship between Agricultural Green Development Efficiency and Food Security System in China

Food security and agricultural green development are fundamental issues concerning the survival of mankind. To realize the coordinated development of them is of great significance to promote the green transformation of agriculture and ensure national food security. However, few studies have analyzed the coupling relationship between agricultural green development and food security. Therefore, this study complements the research on the coupling relationship between them. First, we use the agricultural panel data of 31 provinces in China from 2010 to 2021 to measure the agricultural green development efficiency and food security level by Super-SBM model and entropy weight method. Then, the coupling relationship and spatiotemporal evolution of the two are analyzed by coupling coordination degree model. The results show that, first of all, China's agricultural green development efficiency and food security level have improved overall, but there are regional differences. Secondly, the degree of coupling and coordination between the two is significantly improved, and most provinces develop from the break-in stage to the coordination stage, and the regional differences are relatively reduced. Finally, Beijing, Shanghai and other developed areas are mostly of the lagging food security type, Liaoning, Shandong and other major grain producing areas have reached a high degree of coordination, while Tibet, Qinghai and other western regions are still in the break-in stage. According to the development situation of different regions, corresponding suggestions are put forward. For regions with lagging food security type, the government should give full play to the application of science and technology in agriculture and promote green and low-carbon planting technologies. For regions with low coupling and coordination degree, the government should improve support policies and build a collaborative operation system, adjust planting structure to improve land utilization rate and food security level. Finally, the government should work together to build agricultural industrial parks and give full play to the leading role of competitive provinces to achieve common development.

Retro analysis of the development of the aviation security system in Ukraine

Various measures to ensure the safety of civil aviation flights in Ukraine from the time of its inception to the present are considered in a historical aspect at certain stages of activity, mainly organizational, scientific and technical and personal factors regarding the minimization of risks in civil aviation. The article confirms that at all stages of the more than 100-year history of civil aviation of Ukraine, various measures were constantly taken to ensure flight safety and minimize risks in aviation, especially since the second half of the 20th century. Innovative achievements of designers, scientists, engineers and technicians, training of highly qualified aviation specialists at the national and international level have become the achievement of global aviation. The integration of domestic aviation into the European and world community contributed to the strengthening of aviation safety in Ukraine.

Digital Gram Panchayat

Abstract: While digital Gram panchayat monitoring your security system, there are a lot of factors to take into account. It is the duty of every village to accurately record the details of every villager. Every village uses the same system, and at the end of the year, they all submit a finalized unit record file of villagers. The administrator can show the information if there are plans or possibilities for farmers. The administrator has access to this Web application. The administrator has complete control over the user's information, forms, and profile on the system

Review of Challenges and Innovations in Occluded Facial Recognition in Disguise and Crowd

Abstract: Facial recognition technology is critical in modern security and identification systems, yet it faces significant challenges when dealing with occluded faces, whether due to disguises or in crowds. This review explores the complexities of occluded facial recognition, highlighting the obstacles presented by partial or complete obstruction of facial features by various elements like clothing, accessories, or other people in crowded spaces. These occlusions, intentional or otherwise, complicate the identification process, rendering traditional recognition systems less effective. Recent advancements in machine learning, particularly through Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs), have ushered in innovative solutions that enhance the system's ability to recognize faces even when significant portions are obscured. These developments signify a move towards more adaptable and precise occluded facial recognition technologies, essential for real-world applications where occlusions are unpredictable. The paper discusses ongoing challenges and innovations considerations, various occluded system performance metrics and conclude with way ahead for Researchers and Practioners

Smart Safety with Password Based GSM Module Controlling Circuit Breaker

Abstract: This paper focus on Smart safety with password based GSM module controlling circuit breaker. In our daily life, Safety is the major concern for all activities. Accidental death of a lineman is often read and proven in current scenario. In view, safety measures to protect operators are considered to be very necessary in current working. Using passwords for security, Electrical lineman security systems are used to control circuit breakers. Serious electrical accidents occurs becoming increasingly, when repairing power lines. It is happen due to lack of communication and coordination between maintenance and substation personnel. In the proposed system, the on and off line turning operation with the lineman to ensure the safety of linemen. For that, a secure password is required to operate the breaker operation and maintenance point of view. First request is registered and password is sent to the line operator's mobile phone and GSM module by AVR microcontroller. Entered password and password received by the GSM receiver are compare. If password is match, the operation of circuit breaker will be smoothly operate. The operator gave three chances for incorrect password otherwise message will appear on the LCD display for security purposed. It also sent the message control room regarding unauthorized access to the system.

Smart FIR: Blockchain Based FIR Lodging

Abstract: In order to effectively deal with major crimes like murder, robbery, rape, and kidnapping, we need a secure system for case reporting. In general terms, victims or proxies complete and file e-initial reports (e-FIRs) online to report cybercrime offenses known as electronic crimes (e-crimes). This approach has some drawbacks which include susceptibility to cyberattacks and fraudulent entries. At the same time, scholars are examining various means of improving traditional ways based on technological advancements. One of the possible alternatives is the utilization of digital currencies like Bitcoin while dealing with criminals involved in electronic fraud. Ethereum has a blockchain which has raised curiosity due to its smart contract mechanism. Improvement in data integrity and prevention of fake registrations are the main goals that are meant to be achieved by incorporating Ethereum into the e-FIR system. Security plus reliability are provided for in high measures by the decentralized and immutable nature of the blockchain. This implies that after adding a record to it, it cannot be altered minus agreement from the network, giving it a high reputation based on how safe it is. The use of blockchain in smart cities, and more specifically the smart contracts in Ethereum, may revolutionize how major crimes are reported and recorded as it can offer much better transparency and information integrity in the eyes of both the public and the police

Design and Implementation of an IOT-Based Anti-Theft Mat Using Arduino Nano, ESP32 Camera, and Piezo Sensors

Abstract: As urbanization and crime rates surge, conventional anti-theft security systems face vulnerabilities. This project introduces an innovative solution – the IoT-based Anti-theft Flooring System utilizing ESP32 camera and Arduino Nano. By monitoring floor movement, the system ensures comprehensive security. Activated when occupants leave, the system detects intruders through a secure flooring tile, triggering an alert via IoT.The ESP32 & Arduino Nano powered setup includes a Piezo sensor. Upon detecting an intruder, the system captures and transmits images. This novel approach enhances home security by counteringevasion tactics and providing real-time images to homeowners.

Unusual Human Activity Recognition in ATM’S

Abstract: This paper addresses the imperative need to enhance Automated Teller Machine (ATM) safetyand security through the integration of deep learning and OpenCV. Leveraging Convolutional Neural Networks (CNNs) for feature extraction and Siamese Networks for anomaly detection, our approach aims to fortify ATM systems against threats like skimming and unauthorized access. Realtime image processing with OpenCV facilitatesefficient preprocessing. The study critically examines existing methodologies, highlighting vulnerabilities to adversarial attacks and environmental variations. Through rigorous evaluation and adversarial testing, this research of ATM security systems, fostering a more secure financial landscape.

Security protocols in healthcare: A comprehensive study of AI-Enabled IoMT

The Internet of Medical Things IoMT has driven a paradigm shift in the way things have been done traditionally within medical Healthcare practices and this is evidence that Smart Personal systems (Health Care) are shaping up with mighty forces. By merging these technologies, IoT has transformed data communication and provided the means to directly interconnect medical devices and sensor systems. The use of advanced technologies like Artificial Intelligence( AI), Machine Learning, Deep Learning, and Federated Learning has highly equipped and transformed the IoMT’s performance. These integrations also raise concerns on data privacy and security, and addressing these challenges become more important as without proper security and privacy measures people can face serious disruption both online and offline leading to upcoming catastrophes already waiting at its end line. This chapter looks into the intricacies of a highly secure communications system powered by AI, specifically in the age where disasters like COVID-19 [10], emphasizes the need for increased security on data transmission as well as storage. IoMT has wide applications for integrating remote patient monitoring, enhancing user satisfaction and interdimensional hospitalization rates, we delve into how IoMT works in conjunction with machine learning, particularly federated Learning intends to address any possible concerns. The chapter begins with IoMT systems, including IoMT ecosystems, implantable medical devices IMDs, and Internet-worn gadgetry. Special attention is given to the AI-powered Intrusion Detection Systems (IDSs) in IoMT environments, while network security remains the research focus helping practitioners to implement safety measures enterprise-wide; analysis, comparison, and evaluation all encompass machine learning, deep learning mechanisms as precautions against potential attacks. We will also consider another study on the services of an AI-provisioned lightweight protocol communication system that ensures data authenticity and responds to diverse security implications in IMD. The chapter makes a U-turn by alluding to internet wearable devices, pointing out federated learning and blockchain reinforcements that indicate the need for an integrated framework. The amalgamation of future technologies supports the ethical and secure integration of IoMT. Collaboration, as a guiding principle, compels healthcare practitioners and policymakers to partner efficiently to guide the future where the security of health is ever easier. In the end, a summary of modern ethical and regulatory paradigms in IoMT is provided, emphasizing that there could be an urgent need for new laws.

Leveraging Cryptographic Hash Functions for Credit Card Fraud Detection

Credit card fraud remains a significant challenge in the financial industry, posing substantial financial losses to both consumers and businesses. Traditional fraud detection methods often rely on rule-based approaches and statistical models, which may struggle to keep pace with evolving fraud tactics and sophisticated cyber threats. In this paper, we propose a novel approach to credit card fraud detection leveraging cryptographic hash functions. Cryptographic hash functions offer robust security guarantees, including collision resistance and preimage resistance, making them well-suited for ensuring the integrity and authenticity of transaction data. Our proposed system employs cryptographic hash functions, such as SHA-256, to generate unique hash values for credit card transactions. These hash values serve as digital fingerprints of the transaction data, enabling secure verification and auditing of transactions on the blockchain. We conducted experiments using a dataset of 100,000 credit card transactions, evaluating the performance of our system in terms of accuracy, precision, recall, and F1-score. The results demonstrate the effectiveness of our approach in accurately identifying fraudulent transactions while minimizing false positives. Furthermore, we discuss the implications of our findings and explore future research directions, including the integration of advanced cryptographic techniques and blockchain technology to enhance the security and privacy of credit card transactions. Overall, our study underscores the importance of cryptographic hash functions in building robust and secure fraud detection systems capable of combating emerging fraud threats in the digital era.

CSO-DQN: Circle Search Optimization-based Deep Q-Learning Network for Intrusion Detection System in Cloud Environment

In general, the attackers probably create security-related threats from diverse insecure applications. These attackers execute various nasty activities like establishing hidden rootkit processes, hindering host-based security systems, changing the characteristics of applications, etc. The attackers utilize compromised tenant virtual machines for producing attacks with minor changes to avoid accurate prediction. To deal with these shortcomings, we proposed an efficient novel Deep Q-learning network-based circle search (DQL-CS) algorithm to detect intrusion in a cloud environment. The dataset is pre-processed initially by employing the Term Frequency-Inverse Document Frequency (TF-IDF) approach, which sequentially organizes normal and intrusion traces, and then the collected feature sequences are extracted using n-gram sampling. This process makes the classifier enhance the detection and classification performance. Followed by the extraction process, the classification process is executed using the proposed Deep Q-learning network-based circle search (DQL-CS) algorithm. The proposed DQL-CS algorithm accurately identifies and categorizes the attacks as two different classes normal and malevolent. Then the applications generating the malicious behavior are examined by the cloud admin using an alert generation system and are isolated automatically. The developed intrusion detection system is evaluated using three different malware databases such as the UNM dataset, windows malware dataset, and KDD99 dataset as the input. The accuracy rate of the UNM dataset of the proposed DQL-CS method is higher than other conventional approaches.

Exploring healthcare workers’ experience and barriers in disclosing patient safety-related incidents: A qualitative study in Indonesian hospitals

ABSTRACT Background Open disclosure of patient safety incidents is a strategy to enhance patient safety by preventing future occurrences. This study aimed to identify factors that facilitate or hinder the implementation of open disclosure in Indonesian hospitals. Methods Using a qualitative descriptive research method, 12 healthcare professionals with at least one year of experience and prior incident disclosure experience participated in online interviews. Thematic analysis revealed that open disclosure practices are uncommon in Indonesian hospitals. Results The findings suggest that open disclosure practices are not common practice in Indonesian hospitals. Key issues include unclear responsibility for disclosure, determining which incidents should be disclosed, understanding patient and family responses, implementing follow-up procedures and addressing barriers such as cultural factors, fear of disclosure, lack of protection and insufficient awareness and training among healthcare professionals. Conclusion Promoting open disclosure can improve patient-provider relationships and patient safety. Therefore, healthcare organizations, policymakers and educational institutions should collaborate to foster open disclosure, safeguard patients and educate healthcare professionals, leading to a more secure, transparent and patient-centric healthcare system.

THE REAL TIME HIGH PRECISION CRIMINAL FACE RECOGNITION FOR ATM SECURITY IN DEEP LEARNING

The proposed ATM security system, utilizing composable deep face recognition, emphasizes user protection during transactions with a specific focus on eye retina accuracy. In the event of a potential threat identified through real-time face detection and tracking, an automatic alert is initiated. Prioritizing precision, the system employs advanced face detection algorithms for accurate location and tracking, even in dynamic scenarios. A down sampling technique ensures efficient processing of facial data, crucial for meticulous identification in crowded ATM locations. The integration of a face tracking ID unit enhances accuracy, providing continuous monitoring and persistence in identification, particularly focusing on the distinctive patterns in the eye retina. The scoring method, rooted in face tracking and embedding distance, significantly elevates user identification reliability, minimizing the risk of false positives, especially in criminal identification based on unique eye retina features. In response to potential threats, the system promptly generates automatic alerts and dispatches secure emails to relevant entities, including the affected card user, the respective bank, and cybersecurity authorities. These emails include comprehensive information, such as a high-precision image of the detected criminal's face, detailed facial recognition data, and a thorough summary of their known criminal history. This accuracy-driven and retina focused approach enhances safety, emphasizing continuous refinement and adherence to privacy and ethical considerations during implementation. KEYWORDS: Crime prevention, down-sampling, face recognition.

Pedestrian Abnormal Behavior Detection System Using Edge–Server Architecture for Large–Scale CCTVEnvironments

As the deployment of CCTV cameras for safety continues to increase, the monitoring workload has significantly exceeded the capacity of the current workforce. To overcome this problem, intelligent CCTV technologies and server-efficient deep learning analysis models are being developed. However, real-world applications exhibit performance degradation due to environmental changes and limited server processing capacity for multiple CCTVs. This study proposes a real-time pedestrian anomaly detection system with an edge–server structure that ensures efficiency and scalability. In the proposed system, the pedestrian abnormal behavior detection model analyzed by the edge uses a rule-based mechanism that can detect anomalies frequently, albeit less accurately, with high recall. The server uses a deep learning-based model with high precision because it analyzes only the sections detected by the edge. The proposed system was applied to an experimental environment using 20 video streams, 18 edge devices, and 3 servers equipped with 2 GPUs as a substitute for real CCTV. Pedestrian abnormal behavior was included in each video stream to conduct experiments in real-time processing and compare the abnormal behavior detection performance between the case with the edge and server alone and that with the edge and server in combination. Through these experiments, we verified that 20 video streams can be processed with 18 edges and 3 GPU servers, which confirms the scalability of the proposed system according to the number of events per hour and the event duration. We also demonstrate that the pedestrian anomaly detection model with the edge and server is more efficient and scalable than the models with these components alone. The linkage of the edge and server can reduce the false detection rate and provide a more accurate analysis. This research contributes to the development of control systems in urban safety and public security by proposing an efficient and scalable analysis system for large-scale CCTV environments.

From Division to Coordination: A Study on the Power of Urban Rural Integration of the Minimum Living Security System from the Perspective of Multi Source Flow

For a long time, China's minimum subsistence guarantee system has suffered from the problem of large differences between provinces and cities, and between cities and rural areas within provinces and cities, and in this context, some regions have gradually implemented the urban-rural integration policy and have gained some experience. Based on the theory of multiple streams, it can be found that the convergence of problem or policy streams can open a window of opportunity for policy change, and in China, the political environment and the party's philosophy of governance can also provide reliance on and support for policy change, and the coupling of the three streams provides the conditions for the promotion of the process of urban-rural integration. On this basis, countermeasures are proposed to promote the urban-rural integration of the minimum subsistence guarantee system in three aspects: paying attention to the emergence of problems, exploring policy options and adhering to the political philosophy.

Synergizing AI, IoT, and Blockchain for Diagnosing Pandemic Diseases in Smart Cities: Challenges and Opportunities

The advent of smart cities has paved the way for transformative advancements in healthcare, particularly in the domain of disease diagnosis. In the wake of the COVID-19 pandemic, accurate and timely identification of Pandemic diseases has become paramount. This paper explores the challenges and opportunities in synergizing Artificial Intelligence (AI), Internet of Things (IoT), and Blockchain technologies for diagnosis of Pandemic diseases in smart cities. This study provides an overview of each technology and its relevance to sustainable healthcare in smart cities, emphasizing its potential for analyzing medical data and making informed decisions. We also explore how IoT devices can contribute to disease surveillance, enabling real-time data collection and remote healthcare. Additionally, we discuss the potential of Blockchain in ensuring secure and transparent healthcare systems. Following, the paper study the synergistic potential of integrating AI, IoT, and blockchain, emphasizing how their combined strengths can enhance the accuracy, efficiency, and security of COVID-19 diagnosis systems in smart cities Moreover, the paper highlights the challenges in integrating these technologies and the opportunities for research and implementation, underlining the significance of synergizing AI, IoT, and Blockchain in disease diagnosis in smart cities. The findings demonstrate that the convergence of AI, IoT, and blockchain can enhance the speed and accuracy of diagnosing Pandemic diseases, leading to more effective containment and management strategies.