Home Security Research Articles

Door lock system based on internet of things and Bluetooth by using Raspberry Pi

Recently, there are a large number of smart lock systems that can be used in the workplace or home, but many of them depend on finding an external key, password, fingerprint, and facial recognition, where these systems suffer from weakness as the physical key may be forgotten in a place, the password can be memorized which is hard for some peoples, and the fingerprint system sometimes does not work on people who suffer from chronic diseases, such as people with diabetes and for each of these systems the person needs a free hand so that he/her can open the lock which means that is if the person carries something he/her needs to be placed somewhere. The facial recognition system cannot recognize the person's face when the place has poor lighting. This paper proposes a home door lock system that relies on the internet of things (IoT) technology and portable electronic devices Bluetooth such as a smart mobile using the device Bluetooth media access control (MAC) address as a key and the IoT for monitoring, which leads to a door lock that does not require a free hand. The proposed lock system outcome can be a good candidate for home security applications.

A Comparative Expected Value Analysis Study to Determine the Cost Benefit or Cost Effectiveness of Early Discharge, Medical Transport, Home Health as Well as Home Care Devices, Services and Technologies in The United States

It was found that Amazon products (Alexa™, Echo™, Halo™), EMRs, fire extinguishers, genome sequencing test, on-line pharmacies, remote patient monitoring, provided economic value, while emergency medical service membership programs, fire alarm subscription services, helicopter emergency medical services, home fire insurance policies, home fire sprinklers, and home security systems were not found to have provided economic value. Tele-health (virtual office visits) would provide economic value if most tele-health visits replaced existing in-person visits and the low cost virtual didn’t drive demand for unnecessary visits. Robotic surgery technologies provide economic value if fully utilized (high patient demand to reduce overhead costs per procedure) in a facility with little excess capacity as long as it did not compete with non-robotic surgical offerings at the present facility. AEDs provide economic value if appropriately placed in high demand locales based on future probability of use.

A puf-based three-party authentication key establishment scheme for fog-enabled smart home

With the rise of Internet of Things (IoT), the smart home is another emerging concept and application of IoT, where security and private data of devices are important. In this paper, fog computing is applied to the smart home environment, where fog can provide many smart features and services to the smart home. Fog computing has many advantages, such as low latency and real-time interaction. However, when fog computing is combined with smart home, it also faces some security threats: first, some fog nodes and smart home devices are deployed in public places, vulnerable to damage or theft by attackers, not considered fully trusted, and vulnerable to device loss/theft attacks, impersonation attacks, and message tampering attacks, etc. These threats can lead to adversaries controlling devices in the smart home or modifying messages to make smart home devices execute wrong commands, causing irreparable damage; Second, the smart home system should have good real-time interaction, and the authentication process using the low latency feature of fog computing should not be involved by the cloud. Considering these, it is necessary to design a secure and effective fog-enabled smart home authentication system that is secure against various known attacks, especially when the fog node is not fully trusted or the smart home device is captured as well. Finally, the authentication scheme should also be lightweight due to the limited resources of many smart home devices. To address these issues, this paper proposes a lightweight authentication scheme for the fog-enabled smart home system. It also employs a physical unclonable function to achieve mutual authentication among three parties: smart home devices, fog nodes and users. Formal security analysis under the Real-Or-Random model shows that this scheme is provably secure. And informal security analysis shows that our scheme is robust against various known attacks. At the same time, the proposed scheme requires less computation cost (8.239 ms) and is approximately 40% to 390% faster than existing related schemes. Although the communication cost is slightly higher (4512 bits), it is reasonable because the proposed scheme implements fog/gateway node compromised attack that has not been achieved by any other existing related schemes.

Arduino based smart home security design using biometric recognition

As a standard feature, smart homes have a lock door that requires a password to open. However, burglars can simply defeat this security measure, and it is impossible to identify the user or person who opens the lock door. The authors are interested in building a system with the name research design of an Arduino-based smart home security system employing biometric recognition based on the aforementioned issues. The purpose of this study is to identify the performance requirements and design of an Arduino-based biometric home security system. This study is an engineering study that tries to identify the performance and design requirements for an Arduino-based smart home security system. The mechanical shape of the tool was created using the performance specification. The properties of the sensor, as well as the accuracy and precision of the tool, serve as the basis for the design specifications. The precision value of each sensor, namely the fingerprint sensor, is extremely good, while the precision values of the PIR sensor and magnetic sensor are 99.56% and 97.87%, respectively. The accuracy value of the sensor on the tool, primarily the fingerprint sensor, is very good.

A Fuzzy Logic Approach and Path Algorithm for Time and Energy Management of Smart Cleaning Robots

Smart home technologies (SHM) or devices provide some degree of digitally connected, automated, or enhanced services to building occupants in residential areas and have been becoming increasingly popular in recent years. SHM have the potential to improve home comfort, convenience, security, and energy management. Different technologies are used to equip household parts for smarter monitoring, movement, and remote control and to allow effective harmonic interaction between them. Especially, energy management and path-planning algorithms are some of the important problems for such technologies to get optimum efficiency and benefit. Smart vacuum cleaning robot is one of the applications of such devices with various functions. These cleaning robots have limited battery power and battery sizes, thus effective cleaning is critical. Additionally, the shortest / optimal path planning is essential for the efficient operation of effective cleaning based on the battery time. In this article, two distinct algorithms, which are Search algorithm and CSP algorithm are utilized to obtain distinct optimal minimum path lengths for keeping the home's total dirt level as low as possible. Depending on various types of linguistic, abstract, or perceptual variables, these algorithms are not enough for the energy management of the battery. Therefore, the fuzzy logic-based inference system is proposed for obtaining the charge durability of battery of the cleaning robot, in addition to these algorithms. The inputs affecting the charge durability are considered as floor type, dirt level and the width of area for the fuzzy approach.

IoT – Application and Architecture

Abstract––The Internet of things (IoT) describes physical objects (or teams of such objects) with sensors, process ability, software, and alternative technologies that connect and exchange information with alternative devices and systems over the net or alternative communications networks. net of things has been thought of a name as a result of devices don't ought to be connected to the general public net, they solely ought to be connected to a network and be severally available. the sphere has evolved because of the convergence of multiple technologies, as well as omnipresent computing, goods sensors, progressively powerful embedded systems, and machine learning. ancient fields of embedded systems, wireless device networks, management systems, automation (including home and building automation), severally and put together change the net of things. within the shopper market, IoT technology is most similar with merchandise relating the construct of the "smart home", as well as devices and appliances (such as lighting, thermostats, home security systems, cameras, and alternative home appliances) that support one or additional common ecosystems, and may be controlled via devices related to that system, like goodphones and smart speakers. IoT is additionally utilized in tending systems. Keywords––IoT, embedded system, computing, Internet

BUILDING SECURITY SYSTEM USING GP2Y0A21YK0 INFRARED SENSOR AND ARDUINO UNO

The use of infrared radiation as a home security system is one of the technology improvisations that utilize physics phenomena. This research focuses on the designing of simple control systems based on Arduino Uno and GP2Y0A21YK0F infrared sensors along with other components. The working principle of this system starts when the IR sensor detects moving objects in the vicinity of the house in which infrared radiation is reflected and this gives outputs in the form of distance value from the display as well as movement and sound from servo to inform the occupants of the object’s whereabouts. Data obtained from the display system showed an approximation of the real distance value. Therefore, occupants can know the distance of the moving object.

IoT Enabled Model with Virtual Assistant for Smart Home Applications

The Internet of Things (IoT) has emerged as a prominent technological opportunity, offering numerous professional prospects. It revolves around establishing wireless communication standards between devices and appliances to enhance daily human life. This particular project aims to create a secure and automated home environment by monitoring and controlling various home appliances using Wi-Fi as the primary communication protocol. Traditionally, wireless communication techniques like Wi-Fi and Bluetooth have been utilized for home automation. However, these methods have limitations such as short range and high costs. To overcome these challenges, this paper proposes the techniques of comfy home automation involve the efficient management of appliances like lights and fans, which not only provide convenience but also save time, reduce the need for manual intervention, enhance home security, and send SMS alerts to the owner in case of fire accidents or gas leakages through a dedicated mobile application. Additionally, it aims to train the virtual assistant to engage in enjoyable interactions with the homeowner, further enhancing the user experience. To implement this, this work utilizes the "NodeMCUESP8266" module, which will serve as the foundation for our system. The integration of this technology will prove especially beneficial for individuals who are physically challenged, elderly, or frequently away from home due to their busy schedules. By leveraging the power of IoT and mobile applications, this paper suggests a smart home automation solution that caters to the specific needs and preferences of homeowners, ultimately enhancing their quality of life.

Non-Invasive Monitoring of Vital Signs for the Elderly Using Low-Cost Wireless Sensor Networks: Exploring the Impact on Sleep and Home Security

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.

The Analysis of the Relationship between residents’ Perceived Probability of Property Victimization in Dwellings and the Level of Home Security Systems

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.

Fortifying Smart Home Security: A Robust and Efficient User-Authentication Scheme to Counter Node Capture Attacks.

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.

Smart Floor Mats for a Health Monitoring System Based on Textile Pressure Sensing: Development and Usability Study.

The rise in single-person households has resulted in social problems like loneliness and isolation, commonly known as "death by loneliness." Various factors contribute to this increase, including a desire for independent living and communication challenges within families due to societal changes. Older individuals living alone are particularly susceptible to loneliness and isolation due to limited family communication and a lack of social activities. Addressing these issues is crucial, and proactive solutions are needed. It is important to explore diverse measures to tackle the challenges of single-person households and prevent deaths due to loneliness in our society. Non-face-to-face health care service systems have gained widespread interest owing to the rapid development of smart home technology. Particularly, a health monitoring system must be developed to manage patients' health status and send alerts for dangerous situations based on their activity. Therefore, in this study, we present a novel health monitoring system based on the auto-mapping method, which uses real-time position sensing mats. The smart floor mats are operated as piezo-resistive devices, which are composed of a carbon nanotube-based conductive textile, electrodes, main processor circuit, and a mat. The developed smart floor system acquires real-time position information using a multiconnection method between the modules based on the auto-mapping algorithm, which automatically creates a spatial map. The auto-mapping algorithm allows the user to freely set various activity areas through floor mapping. Then, the monitoring system was evaluated in a room with an area of 41.3 m2, which is embedded with the manufactured floor mats and monitoring application. This monitoring system automatically acquires information on the total number, location, and direction of the mats and creates a spatial map. The position sensing mats can be easily configured with a simple structure by using a carbon nanotube-based piezo-resistive textile. The mats detect the activity in real time and record location information since they are connected through auto-mapping technology. This system allows for the analysis of patients' behavior patterns and the management of health care on the web by providing important basic information for activity patterns in the monitoring system. The proposed smart floor system can serve as the foundation for smart home applications in the future, which include health care, intelligent automation, and home security, owing to its advantages of low cost, large area, and high reliability.

Enhancing Smart Home Security: Anomaly Detection and Face Recognition in Smart Home IoT Devices Using Logit-Boosted CNN Models.

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.

Real Home Automation

Real home automation is a system that allows for the remote control and monitoring of various household appliances and devices, such as lights, thermostats, and security systems, through a central hub or smartphone app. This technology can increase energy efficiency, enhance convenience, and improve overall home security.

Smart Home Berbasis Internet of Things (IoT) Dalam Mengendalikan dan Monitoring Keamanan Rumah

Smart Home or smart home is a system built with the help of a computer to integrate and control home devices automatically and efficiently. The development of "Smart Home" technology is the impact of the development of technological advances that aim to help people manage their daily activities in controlling electronic equipment and monitoring the condition of the house. This home automation system was developed with the aim of supporting electricity and water saving programs. Smart home technology developed, both in home automation systems and security will be controlled centrally by the microcontroller. This study aims to build an electronic device control system at several location points automatically by sending SMS from the cellphone to the control module. This device system will also connect the control module via the internet network so that the Internet of Things (IOT) function is built. Arduino-based Smart Home facilitates home security systems by installing Infrared sensors to detect moving objects, MQ-2 Sensors to detect gas leaks, DHT11 Sensors to detect temperature and humidity, Ultrasonic Sensors to measure the water level in water reservoirs.

“Tec-House” WEBCAM-BASED REMOTE SENSING SYSTEM FOR HOME AND BUILDING SECURITY USING THE HAAR CASCADE METHOD

A home security system is something that every home owner must pay attention crimes such as burglary often put homeowners at risk. Therefore we need a tool that can bring together automatically remotely to protect the house. The system worked on in this article is a remote sensing system based on webcam. The method used in this sensing system uses the haar cascade classifier method. The results obtained from this remote sensing system are for the implementation of the system on homeowner data sets with 98% results, while for non-home owner image data sets with 96% results. From the results of using a webcam-based remote sensing system using the Haar Cascade Classifier method it can be implemented properly and the average error is 97%. The existence of this Tec-House tool can reduce the crime of theft in a house or building.

The Role of Recurrent Convolutional Neural Network in IoT for Building a Security Artificial Intelligence and Home Assistance System

The Role of Recurrent Convolutional Neural Network in IoT for Building a Security Artificial Intelligence and Home Assistance System

SeniorSentry: Correlation and Mutual Information-Based Contextual Anomaly Detection for Aging in Place.

With the ever-growing reliance on IoT-enabled sensors to age in place, a need arises to protect them from malicious actors and detect malfunctions. In an IoT smart home, it is reasonable to hypothesize that sensors near one another can exhibit linear or nonlinear correlations. If substantiated, this property can be beneficial for constructing relationship trends between the sensors and, consequently, detecting attacks or other anomalies by measuring the deviation of their readings against these trends. In this work, we confirm the presence of correlations between co-located sensors by statistically analyzing two public smart-home datasets and a dataset we collected from our experimental setup. Additionally, we leverage the sliding window approach and supervised machine learning to develop a contextual-anomaly-detection model. This model reaches a true positive rate of 89.47% and a false positive rate of 0%. Our work not only substantiates the correlations but also introduces a novel anomaly-detection technique to enhance security in IoT smart homes.

Smart Lock Technology: Developing and Enhancing Home Security using Android-Based Controlled Door Locking App’s

This study presents an overview of the study on the implementation and potential of a novel Android-based controlled door locking app to advance home security in the context of smart homes. The app leverages the capabilities of android devices to offer remote access and management of door locks, empowering homeowners with seamless control over their access points. The study evaluates the app's performance in usability, accuracy, efficiency, portability, security, and maintainability, revealing its commendable capabilities with an average score of 4.17 out of 5. With minor enhancements, the app shows promising potential as a reliable and user-friendly access control solution, driving the evolution of smart lock technology and home security in the modern era.

DDoS Attack Detection in IoT-Based Networks Using Machine Learning Models: A Survey and Research Directions

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.