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Deep learning and the Internet of Things (IoT) are rapidly advancing technologies with wide-ranging applications, notably in areas such as face detection. There is a growing need for sophisticated touchless authentication systems within this domain. While numerous security methods are available, many of them suffer from various shortcomings, including issues like forgotten passwords and the potential transmission of diseases through touch-based authentication techniques. This paper proposes IoT-based intelligent control face detection systems that leverage deep learning (DL) models. These systems are designed to significantly enhance security without relying on manually crafted rules. Face detection plays a pivotal role in safeguarding society, identifying wrongdoers, and bolstering community safety. However, challenges persist, particularly in achieving high accuracy with face recognition control systems in uncontrolled environments and real-time scenarios, such as intersections. In response to these challenges, our paper introduces a novel model that harnesses a combination of deep convolutional neural networks (CNNs) with self-attention mechanisms. Our experimental results demonstrate that this model can rapidly detect whole-scale images in a single forward pass. Notably, our proposed method achieved an outstanding accuracy rate of 99.7%. In comparison to existing state-of-the-art methods, our approach exhibits superior efficiency. This work showcases how the integration of IoT and DL, particularly with the use of a CNN with self-attention, outperforms other CNN-based approaches in terms of both speed and accuracy. [ABSTRACT FROM AUTHOR]

Home Security Systems (HSSs) have received much attention and have been widely adopted for practical deployment. However, detection and warning accuracy still need to be improved, along with the range of the realm, making it challenging to satisfy the user demands. This study proposes a security monitoring system based on mobile robots and the Internet of Things (IoT), allowing users to monitor and control devices remotely. The mobile robot integrated sensor systems and surveillance cameras are utilized for unauthorized early intrusion detection and to give users instant warnings. The data collected by the robot were stored on the Firebase Cloud server, and a mobile application and a Telegram interface were integrated to manage and control the system. In addition, adaptive motion control was adopted to correct errors in the robot’s trajectory. The implementation results proved that this system operated effectively with a minimal response delay of 0.87–1.67 s and a high detection accuracy (96.25%) in two experimental cases, which makes it suitable for real-time applications. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has revolutionized individuals’ homes with smart devices, but it has also brought security worries due to the huge amounts of data they generate. This study aims to uncover common security problems, like malware, cyber-attacks, and data storage flaws, in such smart setups. To tackle these issues, this study suggests beefing up security measures and educating users about safe device practices. A new approach was followed in this study, using Convolutional Neural Networks (CNNs) instead of the traditional Natural Language Processing (NLP) methods. CNNs are great at understanding complex patterns in text, especially on platforms like Twitter where messages can be brief and unclear. By applying CNN to analyze Twitter data, specific entities linked to security issues could be pinpointed, giving a deeper insight into smart home security challenges. The findings showed that the employed CNN model was exceptionally efficient at sorting out tweets regarding security problems in smart homes. It achieved an accuracy of around 87%, precision of 76.78%, recall of 82.49%, and F1-score of 84.87% surpassing the other methods it was compared with. These findings underscore the CNN model's effectiveness in accurately classifying security-related tweets in diverse topics within smart living environments. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) represents a network framework comprising identifiable entities that interact through the Internet. One of its applications is the smart home, where household devices can be remotely monitored and controlled. This has led to an increased demand for reliable security solutions in IoT systems. Security presents a significant challenge in IoT smart home devices and must be carefully considered. Unauthorized access to a smart home system, facilitated by means such as jamming or replay attacks, could pose risks by manipulating sensors and controls, potentially allowing unauthorized entry. This review paper concentrates specifically on the security and privacy aspects of IoT smart home access control devices. It begins with a concise overview of smart home security and privacy, then delves into various techniques within the smart home system taxonomy, such as authentication, access control, blockchain, and cryptography-based methods. Furthermore, the paper compares the advantages and disadvantages of these techniques. It also examines various types of attacks on smart home IoT access control systems and evaluates risk factors such as methodologies, attack frequency, severity, probability, and ranking. Finally, the paper discusses challenges, applications, conclusions, and future directions. [ABSTRACT FROM AUTHOR]

With an upsurge of data by global brands to interact/reach prospective clients, the birth of the Internet has today bridged the information gap. Virtualization techniques are today utilized as means to bridge the various lapses in our human processing endeavors. The adoption of tech to perform a variety of functions has since become imperative to ease our daily living as well as seamlessly allow transformations of various kinds to be impacted on our society. Study proposes a virtual key card access with cost-effective and cheap solution for managing access to areas within a facility. We have successfully integrated IoTs, virtual key card access, web-access control, solenoid lock integration, and ESP32-controller to create a comprehensive access control system. Its benefits over traditional key includes better security, user data privacy, system efficiency, and user convenience. The system also provides real-time monitor and control capabilities that will allow administrators to track and manage access to the facility remotely. And in turn, enhancing system's security and efficiency. [ABSTRACT FROM AUTHOR]

With the increasing use of smart homes and IoT devices, security has become a significant concern. This paper presents a method to enhance smart home security using Deep Convolutional Neural Networks (DCNN) and multiple cameras. In this approach, three cameras are used to capture images from different angles, and these images are analysed using DCNNs including VGG16, VGG19 and DenseNet to detect potential intruders. Known for their excellent performance in image classification, these DCNN models aim to improve the accuracy and efficiency of the security system, thereby reducing false alarms and missed detections. Additionally, the system allows authorized individuals to remotely disable the security system, increasing convenience and usability. The proposed method has shown significant improvement in human presence detection and facial recognition, achieving 99.79% accuracy in classifying home occupants and intruders. This performance is superior to alternative models such as SVM, KNN, and complex decision trees. This paper introduces a new method that integrates multiple cameras with DCNN to boost the performance of security systems. [ABSTRACT FROM AUTHOR]

This study investigates the development and evaluation of an Android-based security system for residential areas, focusing on entrance and exit gate control. Recognizing the limitations of existing systems, we propose an "IoT-based Fingerprint Security Village" application. The system utilizes a prototype development model informed by data collected through interviews, questionnaires, and observations. The application allows residents to monitor and access gates using fingerprint scanners, enhancing security and convenience. To ensure quality, the system underwent rigorous testing based on the ISO 25010 software testing standard. Testing encompassed five key aspects, Functional Suitability: The application achieved a perfect score, indicating it flawlessly performs its intended functions. Portability: The application successfully functioned on all 14 tested Android devices. Performance Efficiency: CPU usage remained exceptionally low (average 0.1%, max 5.5%), and memory usage (average 58.2 MB, max 86.5 MB) demonstrated efficient resource utilization. Security: Testing revealed a low-medium security risk, highlighting areas for potential improvement. Usability: The user-friendliness test yielded a very high score (92%), placing it in the "Very Feasible" category. These results confirm that the "IoT-based Fingerprint Security Village" application effectively meets all evaluated aspects of the chosen software testing standard. [ABSTRACT FROM AUTHOR]

A smart home is a system that has been programmed and can work automatically by utilizing internet of things (IoT) technology, this system can control various electronic devices in the home. This paper presents a design for developing an IoT-based smart home monitoring system with the addition of security features. This research aims to design and develop a smart home monitoring system that uses the IoT which operates via the web and improves the security aspects of the system. This research includes the development of hardware and software that enables efficient and safe monitoring and control of various aspects of the home via smartphone or computer-based devices using resources from solar power plants. This system relies on the use of a Raspberry Pi as a microcontroller and several sensors. In this context has important value in maintaining user security, and privacy and supports the growing development of the smart home technology industry. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) constitutes a sophisticated network that interconnects devices, optimizing functionality across various domains of human activity. Recent literature projections anticipate a significant increase, with estimates exceeding 50 billion connected devices by 2025. Despite its transformative potential, the IoT landscape confronts formidable privacy and security challenges, encompassing intricate issues such as data acquisition, anonymization, retention, sharing practices, and behavioural profiling. Effectively addressing these challenges mandates the development of scalable solutions, innovative management strategies, and adaptable policy frameworks. In this paper, we conduct an exhaustive examination of major IoT applications, alongside associated privacy and security concerns. We systematically categorize prevalent privacy, security, and interoperability issues within the context of the IoT layered architecture. The review highlights current research initiatives focused on developing energy-efficient devices, optimizing microprocessors, and fostering interdisciplinary collaborations to address the challenges in the IoT landscape. To efficaciously manage risks in this dynamic landscape, stakeholders must implement comprehensive strategies that span stringent data protection legislation, extensive user education initiatives, and the deployment of robust authorization and authentication frameworks. This paper aims to empower industry leaders, policymakers, and researchers by providing actionable solutions, not just insights, to navigate the complexities of the IoT landscape effectively. Future research initiatives should prioritize the fortification of security measures for large-scale IoT deployments, the formulation of user-centric privacy solutions, and the standardization of interoperability protocols. By establishing a robust foundational framework, our paper endeavours to spearhead the discourse on IoT applications, privacy paradigms, and security frameworks, paving the way towards a resilient and interconnected future. [ABSTRACT FROM AUTHOR]

Internet of things (IoT) technology must provide users with benefits that outweigh the costs and risks for it to be widely accepted and adopted. New functionality is continuously added, and costs decrease with the increasing volumes and technological advances; however, the risks also increase as technology becomes increasingly integrated into human lives. This survey study of 251 respondents investigated how users and different user groups perceive IoT security and privacy in the home domain, their trust in the involved parties, and how different types of measures, as suggested by the literature, can affect user adoption. The results indicate that people are particularly concerned about security and privacy and are fearful of breaches and misuse. A large majority of the people do not believe the products to be secure. Less than a third of the users trust manufacturers, service providers, and governments to uphold their privacy; this remarkably hinders IoT adoption. [ABSTRACT FROM AUTHOR]

This article discusses the issue of localizing the Sustainable Development Goals (SDGs) and how this is critical for achieving human security globally. It notes that while the SDGs have been successful in generating consensus around sustainability, progress has been lacking and many targets may not be met. A key obstacle is that the SDGs have not been properly localized, which is essential given that real change needs to happen at the local level. The author argues that a bottom-up, localization-driven approach is needed to complement the existing top-down monitoring approach. This would empower local actors and allow them to develop diverse solutions through connecting and sharing ideas. A proposed solution is a digital platform that is co-created with local stakeholders and facilitates the networking of local actors as well as access to expertise. This could help drive cultural and social change from the bottom-up in support of the SDGs. [ABSTRACT FROM AUTHOR]

Objectives: This exploratory study aims to investigate the relationship between the characteristics of the survey respondents, reported fear of burglary and installed home security measures. Methods: This study analyses secondary data from the Crime Survey for England and Wales 2017/18. The data was analysed using suitable statistical methods: regression modelling and factor analysis. Results: The main findings showed that the socioeconomic status of respondents and the status of the area they live in are the strongest predictors of the installation of deterrence home security measures. The findings further revealed that those of lower socioeconomic status are more afraid of burglary and more likely to have deterrence but not entry prevention home security installed. However, the direct impact of fear of burglary on the deterrence home security measures indicated a decrease in the likelihood of this type of home security measure being installed. This suggests that the affordability of home security measures plays an important role. Conclusion: Participants from lower socioeconomic status are more afraid of becoming victims of burglary and invest in cheaper home security measures to protect their homes, while wealthier participants do not feel the need to protect their homes since they are more likely to live in low-crime areas. [ABSTRACT FROM AUTHOR]

Using the dataset of the first nationwide victimization survey in Azerbaijan, the current paper analyzed whether home security systems have a relationship with the perceived probability of property crime victimization. The ordinal logistic regression analysis was employed. Examination of the distribution of security systems identified that dwelling type, residential location and income had a higher level of significance than the perceived risk of property crime victimization in predicting the level of security systems at home. As to the predictive capacity of security systems in relation to the perceived risk of property crime victimization, the former is statistically significant. Additionally, being a victim of property crime, residential location, age, and perceived neighborhood disorder level predicted the perceived risk of property crime victimization. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this publication is to present the usage of smart doorbells in smart locks. Design/methodology/approach: Critical literature analysis. Analysis of international literature from main databases and polish literature and legal acts connecting with researched topic. Findings: The integration of smart locks into the fabric of smart homes represents a groundbreaking advancement, reshaping the dynamics of security and convenience. These intelligent locks have surpassed traditional mechanisms, ushering in an era where digital authentication and advanced features redefine how individuals secure and engage with their living spaces. Smart locks empower homeowners with unparalleled control and accessibility, eliminating the need for physical keys and introducing heightened security through encryption and biometric identification. Emphasizing their pivotal role in smart homes, the publication highlights the ability of smart locks to remotely monitor and control access, providing unprecedented flexibility, especially in scenarios involving trusted individuals. The seamless integration of smart locks within the broader smart home ecosystem fosters an interconnected environment, enabling holistic automation and enhancing user experience and energy efficiency. While acknowledging challenges such as vulnerabilities and power dependency, the publication underscores the vast advantages of smart locks, ranging from enhanced security to increased home resale value. Tables 1, 2, and 3 provide a comprehensive overview of key features, advantages, and challenges, serving as a valuable guide for navigating the evolving landscape of smart home security. As technology advances, smart locks continue to shape the future of residential living, fortifying the boundaries between physical and digital security. Originality/Value: Detailed analysis of all subjects related to the problems connected with the usage of smart locks in smart home. [ABSTRACT FROM AUTHOR]

With the rapid proliferation of Internet of Things devices in smart homes, the necessity of robust security measures has become clearer than ever. This study aims to apply data analytics to enhance security monitoring within smart home environments. By leveraging the wealth of data generated by IoT devices, the study also aims to create an intelligent system that is capable of proactively identifying and addressing potential security threats through Android mobile devices. Therefore, privacy concerns were addressed through encryption and anonymization methods to protect sensitive information. The study evaluates the effectiveness of the developed security monitoring system through simulated and realistic scenarios, thereby highlighting its ability to detect and mitigate a wide range of security threats. Thus, the research contributes to the development of smart home security by providing a smart, data-driven approach to monitoring security incidents. In the evolving landscape of smart homes, the proposed framework forms a cornerstone for ensuring the safety and privacy of residents within this interconnected ecosystem. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has various applications in practice, such as smart homes and buildings, traffic management, industrial management, and smart farming. On the other hand, security issues are raised by the growing use of IoT applications. Researchers develop machine learning models that focus on better classification accuracy and decreasing model response time to solve this security problem. In this study, we made a comparative evaluation of machine learning algorithms for intrusion detection systems on IoT networks using the DS2oS dataset. The dataset was first processed for feature extraction using the infogain feature selection approach. The original dataset (12 attributes), the dataset (6 attributes) produced using the info gain approach, and the dataset (11 attributes) obtained by eliminating the timestamp attribute were then formed. These datasets were subjected to performance testing using several machine learning methods and test choices (10-crossfold, percentage split). The test performance results are presented, and an evaluation is performed, such as accuracy, precision, recall, and F1 score. According to the test results, it has been observed that 99.42% accuracy detection rates are achieved with Random Forest for IoT devices with limited processing power. [ABSTRACT FROM AUTHOR]

In today's era of Home Automation Systems, the provision of automated secure entry has become a fundamental requirement. Our proposal aims to fulfill this need by introducing a secure home entry system leveraging the Internet of Things (IoT). This innovative solution utilizes a raspberry pi controller along with various associated sensors to construct a smart and secure entry system. To enhance the overall home security system, we have incorporated message notification as a communication method in our proposal. Unlike the existing system that relies on phone number notifications, we recognize the widespread popularity and versatility of current social networks across all generations. Hence, we suggest implementing a home security system that utilizes message notifications. The use of phone numbers in this project offers several advantages. It enables the system to send instant notifications to users, ensuring prompt and secure communication between the user and the home automation system. By utilizing phone numbers, we can achieve efficient and reliable communication for a seamless user experience. [ABSTRACT FROM AUTHOR]

Since 2013, the Universal Credit (UC) welfare reform has been rolling out gradually to overhaul the means‐tested social security system for working‐age people in low‐income households in the United Kingdom. Existing research suggests that UC has had a detrimental impact on the housing security of claimants, with evidence of increases in rent arrears and landlord repossession actions as the reform has been rolled out. However, there is currently a lack of evidence on the impact of UC rollout on the most extreme form of housing insecurity—homelessness. This article addresses this gap in knowledge, using Scottish local authority level data obtained from the Scottish Government on monthly rates of Housing Options approaches and statutory homelessness claims, which is linked to data on the timing of UC rollout within local authorities. The staggered nature of UC rollout (i.e., the fact that it rolled out in different local authorities at different times) is exploited in order to measure its impact within 29 Scottish local authorities using fixed effects regression modelling. The results suggest that UC 'Full Service' rollout, up to March 2019, was associated with increases in Housing Options approach rates, but there was not clear evidence of an increase in rates of working‐age statutory homelessness claims. Redesigning UC to increase its standard allowance and address its long wait periods and harsh sanctions would likely help to protect the housing security of claimants. [ABSTRACT FROM AUTHOR]

Gait has unique physiological characteristics and supports long-distance recognition, so gait recognition is ideal for areas such as home security and identity detection. Methods using graph convolutional networks usually extract features in the spatial and temporal dimensions by stacking GCNs and TCNs, but different joints are interconnected at different moments, so splitting the spatial and temporal dimensions can cause the loss of gait information. Focus on this problem, we propose a gait recognition network, Multi-scale Spatio-Temporal Gait (MST-Gait), which can learn multi-scale gait information simultaneously from spatial and temporal dimensions. We design a multi-scale spatio-temporal groups Transformer (MSTGT) to model the correlation of intra-frame and inter-frame joints simultaneously. And a multi-scale segmentation strategy is designed to capture the periodic and local features of the gait. To fully exploit the temporal information of gait motion, we design a fusion temporal convolution (FTC) to aggregate temporal information at different scales and motion information. Experiments on the popular CASIA-B gait dataset and OUMVLP-Pose dataset show that our method outperforms most existing skeleton-based methods, verifying the effectiveness of the proposed modules. [ABSTRACT FROM AUTHOR]

The global surge in migration has led to diversification in immigrant populations and their migration patterns, even towards countries like Japan, China, and South Korea with traditionally low immigration rates. This study examines the forces driving migration among professional Hispano-Americans. It introduces an analytical framework, inspired by Archer and Bourdieu, that unravels the interplay between agency and structure, utilizing the concept of migratory trajectories to investigates dynamic shifts and subjective dimensions inherent in migration. Using grounded theory, in-depth interviews and a questionnaire survey involving 236 professionals reveal four distinct migration profiles. The data highlights the intricate nature of participants' migration expectations, shaped by tensions like the education-occupation gap, corruption, nepotism, and security concerns in their home countries. These aspirations are influenced by a convergence of economic, socio-cultural, and political factors, emphasizing professional growth, enhanced quality of life, and enriching socio-cultural experiences. Importantly, these expectations lead to a tendency to deviate from conventional migration routes, driven by a highly positive perception of destination countries. The findings shed light on the complex interplay of socio-structural tensions, migration expectations, and resulting trajectories. [ABSTRACT FROM AUTHOR]

A home automation system is essential for promoting a safe and comfortable living environment and notable energy conservation for the user. However, the system's favour had been obstructed by cost, power usage, inadequate security, complexity, and no emergency backup power. Current home automation systems with controllers were limited by their number of ports, fixed architecture, non-durable and non-parallel executions. Keeping this in view, integration of home comfort system, security system, and the automatic load transfer switch features are proposed using the base of Cyclone IV E: EP4CE115F29C7 FPGA Board (DE2-115). The top-level module is developed via Verilog Hardware Descriptive Language (HDL) with the bottom-up technique and used test bench for functional verification via ModelSim-Altera. The PWM method was applied to the lighting system to control the dimming of light through its digital signals via a maximum 500000 counter to improve energy efficiency for the proposed design. In this project, 200Hz pulses are successfully simulated to prevent visible flickering of lights in duty cycle generation. The light intensity of 40% and 100% are verified and successfully generated according to the inputs provided by the status of the LDR sensor and IR sensor. The proposed controller gives correct corresponding outputs to the 13 actuators based on the detected input stimuli. The proposed design utilized a total of 162 (<1%) logic elements, 32 registers, and total pins of 74 (14%). The proposed design successfully integrated the three-sub module and provided control on comfort and security system operations to prevent service failure during power blackout conditions at the top-level and utilized a low ratio of the FPGA. [ABSTRACT FROM AUTHOR]

The security and privacy of smart home systems, particularly from a home user's perspective, have been a very active research area in recent years. However, via a meta-review of 52 review papers covering related topics (published between 2000 and 2021), this article shows a lack of a more recent literature review on user perspectives of smart home security and privacy since the 2010s. This identified gap motivated us to conduct a systematic literature review (SLR) covering 126 relevant research papers published from 2010 to 2021. Our SLR led to the discovery of a number of important areas where further research is needed; these include holisticmethods that consider a more diverse and heterogeneous range of home devices, interactions between multiple home users, complicated data flowbetween multiple home devices and home users, some less studied demographic factors, and advanced conceptual frameworks. Based on these findings, we recommended key future research directions, e.g., research for a better understanding of security and privacy aspects in different multi-device and multi-user contexts, and a more comprehensive ontology on the security and privacy of the smart home covering varying types of home devices and behaviors of different types of home users. [ABSTRACT FROM AUTHOR]

In the modern era, smart home technology is considered as the most growing application of Internet of Things (IoT) as they facilitate users for control home devices remotely. Anonymous and efficient authentication approaches in smart home environments are essential for attaining secure communication as the home and user communication channel is highly vulnerable and insecure. In this paper, Merkle Hash Tree and Strongly Constrained Discrete Hashing Function-based Authentication (MHS-SCDHF-AS) that integrates the transaction history and physical context awareness is proposed for enabling security for Smart Home IoT applications. This MHS-SCDHF-AS prevents the problem of clock synchronization and does not utilize a verification table both during the authentication process. This Strongly Constrained Discrete Hashing Functionis a supervised hashing method which overcomes the limitations of large quantization errors and suboptimal binary codes. It is also capable of learning binary codes and significant in resisting the transmission loss as each information used for verification contains its authentication data in itself. The computation cost and communication cost of the proposed MHS-SCDHF-AS is identified to be lesser than the benchmarked authentication schemes considered for investigation. The formal, informal and model checking-based security investigation of the proposed MHS-SCDHF-AS is also confirmed to be superior over the baseline authentication schemes used for smart home environments. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSN) are useful in medicine for monitoring the vital signs of elderly patients. These sensors allow for remote monitoring of a patient's state of health, making it easier for elderly patients, and allowing to avoid or at least to extend the interval between visits to specialized health centers. The proposed system is a low-cost WSN deployed at the elderly patient's home, monitoring the main areas of the house and sending daily recommendations to the patient. This study measures the impact of the proposed sensor network on nine vital sign metrics based on a person's sleep patterns. These metrics were taken from 30 adults over a period of four weeks, the first two weeks without the sensor system while the remaining two weeks with continuous monitoring of the patients, providing security for their homes and a perception of well-being. This work aims to identify relationships between parameters impacted by the sensor system and predictive trends about the level of improvement in vital sign metrics. Moreover, this work focuses on adapting a reactive algorithm for energy and performance optimization for the sensor monitoring system. Results show that sleep metrics improved statistically based on the recommendations for use of the sensor network; the elderly adults slept more and more continuously, and the higher their heart rate, respiratory rate, and temperature, the greater the likelihood of the impact of the network on the sleep metrics. The proposed energy-saving algorithm for the WSN succeeded in reducing energy consumption and improving resilience of the network. [ABSTRACT FROM AUTHOR]

In smart home environments, the interaction between a remote user and devices commonly occurs through a gateway, necessitating the need for robust user authentication. Despite numerous state-of-the-art user-authentication schemes proposed over the years, these schemes still suffer from security vulnerabilities exploited by the attackers. One severe physical attack is the node capture attack, which allows adversaries to compromise the security of the entire scheme. This research paper advances the state of the art by conducting a security analysis of user-authentication approaches regarding their vulnerability to node capture attacks resulting in revelations of several security weaknesses. To this end, we propose a secure user-authentication scheme to counter node capture attacks in smart home environments. To validate the effectiveness of our proposed scheme, we employ the BAN logic and ProVerif tool for verification. Lastly, we conduct performance analysis to validate the lightweight nature of our user-authentication scheme, making it suitable for IoT-based smart home environments. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a broad concept that encompasses a variety of technologies and applications. As a result, having a comprehensive understanding of the future developments and deployment of this concept in various lateral and vertical markets and following future trend projections is critical. In this way, future studies might focus on numerous issues in areas where the Internet of Things is most widely used. This study focuses on the smart home setting, one of the most prevalent and fastest-growing applications of the Internet of Things idea. This field's current and future growth tendencies have been proven in many categories and capabilities of IoT devices such as comfort and lighting, energy, security, monitoring, media, and household appliances. The number of devices in use, houses with at least one IoT device, total revenue, and market value are increasing. With the positive growth trend of the smart home, the concept of cybersecurity is becoming one of the most critical issues whose overview we included in this research. Research results indicate several cybersecurity challenges raised due to the fast-growing trend of smart home concept that needs to be addressed in the future to prevent users' privacy and usability of various end devices in such environment. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) devices for the home have made a lot of people's lives better, but their popularity has also raised privacy and safety concerns. This study explores the application of deep learning models for anomaly detection and face recognition in IoT devices within the context of smart homes. Six models, namely, LR-XGB-CNN, LR-GBC-CNN, LR-CBC-CNN, LR-HGBC-CNN, LR-ABC-CNN, and LR-LGBM-CNN, were proposed and evaluated for their performance. The models were trained and tested on labeled datasets of sensor readings and face images, using a range of performance metrics to assess their effectiveness. Performance evaluations were conducted for each of the proposed models, revealing their strengths and areas for improvement. Comparative analysis of the models showed that the LR-HGBC-CNN model consistently outperformed the others in both anomaly detection and face recognition tasks, achieving high accuracy, precision, recall, F1 score, and AUC-ROC values. For anomaly detection, the LR-HGBC-CNN model achieved an accuracy of 94%, a precision of 91%, a recall of 96%, an F1 score of 93%, and an AUC-ROC of 0.96. In face recognition, the LR-HGBC-CNN model demonstrated an accuracy of 88%, precision of 86%, recall of 90%, F1 score of 88%, and an AUC-ROC of 0.92. The models exhibited promising capabilities in detecting anomalies, recognizing faces, and integrating these functionalities within smart home IoT devices. The study's findings underscore the potential of deep learning approaches for enhancing security and privacy in smart homes. However, further research is warranted to evaluate the models' generalizability, explore advanced techniques such as transfer learning and hybrid methods, investigate privacy-preserving mechanisms, and address deployment challenges. [ABSTRACT FROM AUTHOR]

Building on the Hodgson–Mokyr debate in this journal (Volume 18, Issue 1, 2022), this article discusses how modern economic growth occurred in pre-Industrial Revolution Britain, with a particular focus on coalition politics and the marginalization of conservative political groups – vetoers to change. Such political marginalization was unusual before the 19th century, when monarchs had substantial political power and land-based conservative groups were their main political allies. This article finds the source of the English exceptionalism in the unique system of non-imperial personal union that Britain then had with the Dutch Republic and Hanover. Under this system, foreigner monarchs chose their local ally in Britain based on the security needs of their home states. It created a significant disadvantage to the Tories, the incumbent conservative groups, while providing a window of opportunity for the Whigs, the opposition group supported by new commercial interests, to form a coalition with the Crown. The long absence of the Tories from power resulted in the incorporation of their constituencies into the Whig-led regime, making the traditional economic interests the regime's 'junior partners', instead of formidable political competitors to the new commercial interests, which was the case before and elsewhere at that time. [ABSTRACT FROM AUTHOR]

Home security systems have been extensively used to protect our property and family, and these products are always equipped with some devices, including indoor and outdoor cameras, infrared motion sensors, human body temperature sensors, and smart locks. Security systems are designed to detect the presence of people or moving objects. However, they have certain limitations. Firstly, these systems are inactive when not turned on, rendering them ineffective during those times. Additionally, false alarms are common during system surveillance, posing a challenge to the system’s overall reliability. A smart surveillance camera has recently been added to the system, but it cannot distinguish between family members and intruders. A fulltime facial recognition system has been proposed in this paper to address the drawbacks of the current security system. The Day Night Surveillance Neural Network (DNSNN), which is a face recognition network based on the optimized EfficientDet, is proposed. The DNSNN provides full-time recognition in this study and divides the system into day and night modes. It uses visible light images in day mode under good light conditions to perceive objects. Under poor light conditions, the camera automatically takes grayscale images with near IR. However, in these images, objects are challenging to recognize, and thus the accuracy rate is reduced. A proposed auto-coloring system is applied to colorize the grayscale images. The colorized images can have a similar hue to the visible light images and are equipped with the same vision. This method can improve system recognition capabilities under dim light. The experimental results show that our proposed approaches recognize family members and intruders under all light conditions and have an identification accuracy of more than 90%. This system can achieve fulltime smart home surveillance. [ABSTRACT FROM AUTHOR]

With the emergence of technology, the usage of IoT (Internet of Things) devices is said to be increasing in people's lives. Such devices can benefit the average individual, who does not necessarily have to have technical knowledge. The IoT can be found in home security and alarm systems, smart fridges, smart televisions, and more. Although small Internet-connected devices have numerous benefits and can help enhance people's efficiency, they also can pose a security threat. Malicious actors often attempt to find new ways to exploit and utilize certain resources, and IoT devices are a perfect candidate for such exploitation due to the huge volume of active devices. This is particularly true for Distributed Denial of Service (DDoS) attacks, which involve the exploitation of a massive number of devices, such as IoT devices, to act as bots and send fraudulent requests to services, thus obstructing them. To identify and detect whether such attacks have occurred or not in a network, there must be a reliable mechanism of detection based on adequate techniques. The most common technique for this purpose is artificial intelligence, which involves the use of Machine Learning (ML) and Deep Learning (DL) to help identify cyberattacks. ML models involve algorithms that use structured data to learn from, predict outcomes from, and identify patterns. The goal of this paper is to review selected studies and publications relevant to the topic of DDoS detection in IoT-based networks using machine-learning-relevant publications. It offers a wealth of references for academics looking to define or expand the scope of their research in this area. [ABSTRACT FROM AUTHOR]

The expansion of IoT devices over the past years has led to the development of smart ecosystems susceptible to cyberattacks. These ecosystems include smart industries and smart home security systems. However, when these devices are widely deployed and generate significant amounts of data, this data must be securely stored on the cloud to prevent it from being compromised by serious cyber attacks. Although traditional security methods partly meet the need to protect the privacy and security of smart IoT ecosystems, their centralized structure and limited computing power make them inefficient. However, recent advancements in blockchain technology have made it possible to employ decentralized architectures to enhance the cybersecurity defense mechanisms of IoT ecosystems. The utilization of blockchain-enabled methodologies is considered one of the most auspicious advancements in the domain of the IoT. These techniques offer a secure and decentralized mode of communication among internet-connected devices. This study presents a blockchain-based novel framework to enhance the cybersecurity measures of smart IoT ecosystem installations. The proposed framework prioritizes the immutability of devices and users present in such environments and incorporates smart contracts to ensure their overall security. Furthermore, the system includes a module for regularly verifying the firmware of connected devices by comparing them to the valid firmware hashes present in the smart contract's storage. The proposed approach has also come up with the idea of blacklisting the malicious IP addresses trying to connect with IoT ecosystems network. [ABSTRACT FROM AUTHOR]

Modern home automation systems include features that enhance security, such as cameras and radars. This paper proposes an innovative home security system that can detect burglars by analyzing acoustic signals and instantly notifying the authorized person(s). The system architecture incorporates the concept of the Internet of Things (IoT), resulting in a network and a user-friendly system. The proposed system uses an adaptive detection algorithm, namely the "short-time-average through long-time-average" algorithm. The proposed algorithm is implemented by an IoT device (Arduino Duo) to detect people's acoustical activities for the purpose of home/office security. The performance of the proposed system is evaluated using 10 acoustic signals representing actual events and background noise. The acoustic signals were generated by the sounds of keys shaking, the falling of a small object, the shrinking of a plastic bag, speaking, footsteps, etc. The effects of different algorithms' parameters on the performance of the proposed system have been thoroughly investigated. [ABSTRACT FROM AUTHOR]

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With the increasing demand for security in various aspects of daily life, including homes, the need for reliable and costeffective security systems has increased. This paper presents an Internet of Things (IoT)-based approach to home security utilizing ultrasonic sensors and buzzer systems. The proposed system, implemented using Arduino microcontrollers, offers a wireless solution for detecting intruders within a specified range using ultrasonic sensors. Upon detecting an object, the system activates a piezoelectric buzzer, effectively acting as an alarm. Additionally, the sensor data is processed to generate a graphical representation, providing visual feedback on obstruction status. A servo motor is employed to rotate the ultrasonic sensor, extending the coverage area. The system's setup, working principle, advantages, drawbacks, and future scope are discussed, highlighting its potential for enhancing home security while offering flexibility for future enhancements and integration with advanced features. [ABSTRACT FROM AUTHOR]