Smart Home Security Research Articles

Knowledge-based Cyber Physical Security at Smart Home: A Review

Smart-home systems represent the future of modern building infrastructure as they integrate numerous devices and applications to improve the overall quality of life. These systems establish connectivity among smart devices, leveraging network technologies and algorithmic controls to monitor and manage physical environments. However, ensuring robust security in smart homes, along with securing smart devices, presents a formidable challenge. A substantial number of security solutions for smart homes rely on data-driven approaches (e.g., machine/deep learning) to identify and mitigate potential threats. These approaches involve training models on extensive datasets, which distinguishes them from knowledge-driven methods. In this review, we examine the role of knowledge within smart homes, focusing on understanding and reasoning regarding various events and their utility toward securing smart homes. We propose a taxonomy to characterize the categorization of decision-making approaches. By specifying the most common vulnerabilities, attacks, and threats, we can analyze and assess the countermeasures against them. We also examine how smart homes have been evaluated in the reviewed literature. Furthermore, we explore the challenges inherent in smart homes and investigate existing solutions that aim at overcoming these limitations. Finally, we examine the key gaps in smart-home-security research and define future research directions for knowledge-driven schemes.

The Future of Smart Home Security: Generative AI and LLMs for Intelligent Event Detection and Personalized Notifications

Abstract—Smart home security cameras are becoming more common, but their usefulness can be diminished by notification fatigue from too many alerts about minor incidents. This paper examines the gaps of existing event detection and notification systems in security cameras and then recommends using Generative AI and Large Language Models (LLMs) to add intelligence which would improve user experience. Generative AI can be leveraged to classify events more accurately and assist with anomaly detection. LLMs can further be used to create notifications that are tailored to the context and personalized to users behavior, helping to reduce notification fatigue and provide meaningful user alerts. The paper also looks into wider applications of these technologies to add intelligence and improve other related experiences like automated video summarization, proactive security measures, and improved privacy controls. The integration of Generative AI and LLMs with smart home security camera systems advances the smart cameras capabilities and offers enhanced security, personalized user experiences. Keywords—Smart home security, Generative AI, Large Language Models (LLMs), Event detection, Anomaly detection, Notification fatigue, Context-aware notifications, Personalized security, Reinforcement Learning from Human Feedback (RLHF), Internet of Things (IoT).

Towards Securing Smart Homes: A Systematic Literature Review of Malware Detection Techniques and Recommended Prevention Approach

The exponential growth of the Internet of Things (IoT) sector has resulted in a surge of interconnected gadgets in smart households, thus exposing them to new cyber-attack susceptibilities. This systematic literature review investigates machine learning methodologies for detecting malware in smart homes, with a specific emphasis on identifying common threats such as denial-of-service attacks, phishing efforts, and zero-day vulnerabilities. By examining 56 publications published from 2019 to 2023, this analysis uncovers that users are the weakest link and that there is a possibility of attackers disrupting home automation systems, stealing confidential information, or causing physical harm. Machine learning approaches, namely, deep learning and ensemble approaches, are emerging as effective tools for detecting malware. In addition, this analysis highlights prevention techniques, such as early threat detection systems, intrusion detection systems, and robust authentication procedures, as crucial measures for improving smart home security. This study offers significant insights for academics and practitioners aiming to protect smart home settings from growing cybersecurity threats by summarizing the existing knowledge.

Deep-Learning-Based Real-Time Passive Non-Line-of-Sight Imaging for Room-Scale Scenes.

Non-line-of-sight imaging is a technique for reconstructing scenes behind obstacles. We report a real-time passive non-line-of-sight (NLOS) imaging method for room-scale hidden scenes, which can be applied to smart home security monitoring sensing systems and indoor fast fuzzy navigation and positioning under the premise of protecting privacy. An unseen scene encoding enhancement network (USEEN) for hidden scene reconstruction is proposed, which is a convolutional neural network designed for NLOS imaging. The network is robust to ambient light interference conditions on diffuse reflective surfaces and maintains a fast reconstruction speed of 12.2 milliseconds per estimation. The consistency of the mean square error (MSE) is verified, and the peak signal-to-noise ratio (PSNR) values of 19.21 dB, 15.86 dB, and 13.62 dB are obtained for the training, validation, and test datasets, respectively. The average values of the structural similarity index (SSIM) are 0.83, 0.68, and 0.59, respectively, and are compared and discussed with the corresponding indicators of the other two models. The sensing system built using this method will show application potential in many fields that require accurate and real-time NLOS imaging, especially smart home security systems in room-scale scenes.

Developing a Micro-Credential Curriculum in IoT for Malaysian Community Colleges: Key Topics and Projects

The Internet of Things (IoT) is a crucial course in Information Technology programs, merging electronic circuit concepts with programming skills to create interconnected devices. This study aimed to design a Malaysian Community College micro-credential curriculum for IoT by identifying relevant theoretical and practical topics and mini-projects. A need for a micro-credential module with suitable instructional materials was identified, enabling learners to apply theoretical concepts through mini-projects. Using the Fuzzy Delphi (FD) method, data from 19 experts via semi-structured interviews were analyzed to design the Fuzzy Delphi instrument. This instrument was distributed to 30 industry experts and academic practitioners, who ranked the elements on a five-point Likert scale. Fuzzy evaluation to establish consensus for inclusion requires an average expert agreement of 75%, a d-construct threshold of 0.2, and an average fuzzy number of 0.5. Consensus was reached for six out of nine theoretical topics and nine out of 13 practical topics. “Fundamental concepts of IoT” was deemed the most critical theoretical topic with 97% consensus, while "basic connectivity of IoT hardware" was the top practical topic with 100% consensus. For mini-projects, experts accepted two out of nine theoretical topics and nine out of 13 hands-on topics. The top authentic projects were “Air Quality Monitoring” and “Smart Home Security”, with 83% and 80% consensus, respectively. This study lays the groundwork for developing learning outcomes and assessment strategies for a micro-credential course on IoT at Community Collegesto equip TVET students with industry-relevant competencies, thus making them more employable.

Unveiling Users' Security and Privacy Concerns Regarding Smart Home IoT Products from Online Reviews

The Internet of Things (IoT) has revolutionized the global market with lifestyle products such as fitness trackers (FT), smart home speakers (SHS), and surveillance and security camera systems (SSCS). While offering convenience, these products also introduce potential security and privacy (S&P) risks to buyers, often going unnoticed. Consumers’ incomplete mental models of the risks involved and the information asymmetry between buyers and sellers only add to the problem. Understanding consumer concerns in online product reviews can play a crucial role in bridging the gap of such information asymmetry. By establishing a balanced flow of information between buyers and sellers, manufacturers can leverage genuine concerns expressed in reviews to enhance product features while educating users about misinformation in reviews. In this study, we collected FT, SHS, and SSCS product reviews from three Amazon domains: the US, the UK, and India. Using a keyword-based search method focused on S&P concerns, we discovered a considerable number of reviews expressing notable concerns regarding security and privacy. Our qualitative analysis revealed that data security is a common concern across all product types. Further, our quantitative analysis exposed significant geographic variations, with the concern ratio being higher in the US than in the UK for all device types and higher than in the Indian domain for security cameras. These findings highlight the need for tailored security measures and user awareness campaigns in different parts of the world to address the identified concerns effectively.

Smart Home Security using Arduino-based Internet of Things (IoTs) Intrusion Detection System

Home intrusion detection systems (IDS) have become increasingly important in our modern community due to their high performance in adequately securing the home environment. Nowadays, there is an incessant increase in the number of theft cases hence a need for an intrusion detection system to detect any intruder around our homes. The aim of This paper is to develop a Smart Home Security using an Arduino-based Internet of Things (IoT) Intrusion Detection System. This security system is built using Arduino Uno, ultrasonic sensor and GSM module for efficient monitoring of intruders and sending of SMS alerts to the homeowners at a point of intrusion which is made possible through the Internet of Things network. The Internet of Things is simply a type of network used to connect anything with the Internet based on stipulated protocols through information sensing equipment to conduct information exchange and communications in order to achieve smart recognitions, positioning, tracing, monitoring, and or administration. The proposed system uses an Arduino microcontroller programed to coordinate the activities of the various components connected together for efficient communication using C++ programming language through the Arduino Integrated Development Environment (IDE). The system is simple and highly efficient as it is capable of detecting any intruder within the monitoring environment and triggering an SMS alert once an intruder is found.

Three-dimensional triboelectric nanogenerator manufacturing using water transfer printing

Triboelectric nanogenerators (TENGs) have been widely studied due to their efficient energy harvesting capabilities and fast electrical response signals. In this study, a processing method based on water transfer printing (WTP) is developed for preparing TENG devices with three-dimensional (3D) structures. With this method, electrodes and ink layers can be integrated onto a variety of 3D objects to form a one-piece structure, which offers advantages including application to 3D surfaces, abrasion resistance, handling ease, adaptability, and decorativeness. The process istaken to prepare a 5 × 5 cm2 single-electrode TENG (IF-TENG), with FEP serving as the negative triboelectric layer and ink serving as the positive triboelectric layer. The TENG’s open-circuit voltage and short-circuit current are 184 V and 1.53 μA, respectively. Additionally, a door handle TENG (DH-TENG) device has been built for practical application demonstrations. The DH-TENG device is used in wireless alarm systems that prevent children from accidentally ingesting medication. It is also used in self-powered guidance systems that illuminate LED arrow lights to help the elderly or children at night. Seamless integration of 3D surface TENG devices using WTP is expected to have applications in smart home systems, smart transportation and security, wearable electronics, and energy harvesting.

UP-Home: A Self-Adaptive Solution for Smart Home Security

Smart home devices are vulnerable to attacks that put their users’ security at risk. Vulnerabilities are discovered very frequently and can expose these devices through unsecured services. Meanwhile, the lack of standardisation in upgrade methods makes smart homes a potentially vulnerable environment. Furthermore, many manufacturers release their products and then abandon them, refusing to support security updates. As a result, security updates are needed to deal with the emergence of new attacks. There are several proposals to promote security in smart homes. However, there are rare solutions where changes for security purposes occur with little or no human intervention. This paper presents UP-Home, a self-adaptive solution that manages the security of smart homes. UP-Home aims to ensure that smart home devices meet the security requirements set by industry standards. The solution can continually identify and mitigate smart home security vulnerabilities. With autonomous computing techniques, UP-Home seeks to ensure the self-protection of devices and, consequently, the entire smart home. With the UP-Home evalu-ation, it was possible to notice significant improvements in the security of the smart home without any human intervention.

Editorial

Dear Readers,  It gives me great pleasure to announce the fourth regular issue of 2024. I would like to thank all the authors for their sound research papers and the editorial board and our guest reviewers for their extremely valuable reviews and suggestions for improvement. These contributions and the generous support of the consortium members enable us to run our journal and maintain its quality. I would also like to thank our broader community for reading and incorporating sound J.UCS papers into their research. I am also very proud to announce that our journal has been included in the Free Journal Network as one of the listed high-quality journals in this field. Still, I would like to expand our editorial board: If you are a tenured associate professor or above with a good publication record, please apply to join our editorial board. We are also interested in receiving high-quality proposals for special issues on new topics and emerging trends. In this regular issue, I am very pleased to introduce six papers by 22 authors from nine countries: Brazil, Czech Republic, Germany, Iran, Jordan, Portugal, Thailand, Turkey, and the USA. In a collaboration between researchers from the Czech Republic and Germany, Sivaramasamy Elayaraja, Sunil Yeruva Vlastimil Stejskal, and Satish Nandipati look into multi-class microscopic image analysis of protozoan parasites using convolutional neural network and perform a study on 4740 microscopic images. André Diegues Fernandes, João Cruz, Miguel Mira da Silva, and Rúben Pereira from Portugal cover a systematic literature review for mapping and integrating security and risk standards. In a collaborative research between Jordan and the USA, Sahar Idwan, Junaid Zubairi, Syed Ali Haider, and Wael Etaiwi introduce their reactive traffic congestion control method based on a hierarchical graph approach. Shahrzad Riahi, Ramtin Khosravi, and Fatemeh Ghassemi from Iran conduct a study on knowledge-related policy analysis in an inference-enabled actor model. Josival Silva, Nelson Rosa, and Fernando Aires from Brazil research and present UP-Home, a self-adaptive solution that manages the security of smart homes by industry standards, and identifies and mitigates smart home security vulnerabilities. Last but not least, Sait Can Yucebas, Sukran Yalpir, Levent Genc, and Melike Dogan from Turkey discuss their research on price prediction and determination of the factors influencing real estate prices using X-Means clustering and CART decision trees. Enjoy Reading! Cordially, Christian Gütl, Managing Editor Graz University of Technology, Graz, Austria

Smart Home Security Design Based on STM32 Microcontroller

This paper addresses the needs in the field of smart home security, based on STM32 microcontroller for smart home security design. Firstly, the background and research significance of smart home security are introduced, and the current situation and development trend of smart home security are briefly summarized. Then, the basic concepts and classifications of microcontrollers are introduced, and the applications and roles of microcontrollers in smart home security are elaborated. Next, the STM32 microcontroller was selected, the system hardware circuit and software program were designed, and the system was implemented and tested. Finally, the research work of this paper is summarized, problems and shortcomings are pointed out, and future research directions and prospects are discussed. Through the research and realization of this paper, the security level of smart home can be effectively improved, and make some contributions to the development of smart home.

Access Control Using Facial Recognition

The field of smart home technology has grown by leaps and bounds over the last few decades. This development has made several new products available that can add convenience and security to facility. People became aware of smart home devices with the introduction of the smart thermostat, but these thermostats were only the beginning to smart homes. Smart devices helps people manage their sched-ules, grocery lists, home lighting and even the home security. These devices work together to make life a bit easier as well as safer. One group of such smart home security devices that are gaining popularity are smart door locks. Smart door locks are also seeing new innovative features being added recently where some companies are adding the capability of facial recognition to the locks. With nothing but the face, it’s now possible for a door lock can be operated. As far back as 2016, manufacturers started talk-ing about smart locks with face recognition. The innovation has continued to grow as several main-stream tech devices with facial recognition like the phones and handheld devices with facial recogni-tion unlock features. In smart door locks, the device uses facial recognition, leveraging advanced processing and analytics to identify a person’s facial attributes at the door. KEY WORDS Access control, facial recognition, door locks, security.

Integration of Machine Learning and Iot based Multi-Layer Wireless Sensor Networks for Seamless Smart Home Automation

The integration of machine learning algorithms with Internet of Things -based multi-layer wireless sensor networks offers a transformative solution to modern smart home automation and security. This paper seeks to explore both the Sensor Data Processing and Machine Learning Components used. In essence, the machine learning model leverages aggregate sensor data to improve the efficiency and safety of modern living environments. Through attention to detail during the preprocessing and feature engineering of sensor data, the system analyzed learned patterns of household activity, including motion intensity, door/window interactions, and normal occupancy. Subsequently, the features were utilized as input variables in a number of models, including Convolutional Neural Networks , Support Vector Machines, Recurrent Neural Networks, and K-Nearest Neighbors. By simulating both vacation and occupant mode, the models processed the available data using to detect potential threats or other deficiencies in the patterns. Overall the results led by the model suggest that the Convolutional Neural Network model is the best amongst all of the aforementioned algorithms. It demonstrated the best accuracy out of all the other models due to the advanced image processing and facial recognition features, solidified by its 98.56% accuracy rate in recognizing a potential intruder. Other models were also effective or reasonably accurate, with the following results: SVMs – 95.45%; RNNs – 93.4%; and KNNs – 91.23%. The evaluation process also involves the calculation of precision, recall, and F1 score, which also indicates the overall strength of the model. The aggregation of these results implies a considerable potential for internet-of-things systems in combination with machine learning methods to cultivate smarter and safer living environments.

IoT based security and privacy implementation in smart home

IoT based security and privacy implementation in smart home

Development and Assessment of Internet of Things-Driven Smart Home Security and Automation with Voice Commands

With the rapid rise of digitalization in the global economy, home security systems have become increasingly important for personal comfort and property protection. The collaboration between humans, the Internet of Things (IoT), and smart homes can be highly efficient. Interaction considers convenience, efficiency, security, responsiveness, and automation. This study aims to develop and assess IoT-based home security systems utilizing passive infrared (PIR) sensors to improve user interface, security, and automation controls using voice commands and buttons across different communication protocols. The proposed system incorporates controls for lighting and intrusion monitoring, as well as assessing both the functionality of voice commands and the precision of intruder detection via the PIR sensors. Intelligent light control and PIR intruder detection with a variable delay time for response detection are unified into the research methodology. The test outcomes examine the average effective response time in-depth, revealing performance distinctions among wireless fidelity (Wi-Fi) and fourth- and fifth-generation mobile connections. The outcomes illustrate the reliability of voice-activated light control via Google Assistant, with response accuracy rates of 83 percent for Thai voice commands and 91.50 percent for English voice commands. Moreover, the Blynk mobile application provided exceptional precision regarding operating light-button commands. The PIR motion detectors have a one hundred percent detection accuracy, and a 2.5 s delay is advised for PIR detection. Extended PIR detection delays result in prolonged system response times. This study examines the intricacies of response times across various environmental conditions, considering different degrees of mobile communication quality. This study ultimately advances the field by developing an IoT system prepared for efficient integration into everyday life, holding the potential to provide improved convenience, time-saving effectiveness, cost-efficiency, and enhanced home security protocols.

Enhancing IoT-Based Smart Home Security Through a Combination of Deep Learning and Self-Attention Mechanism

Enhancing IoT-Based Smart Home Security Through a Combination of Deep Learning and Self-Attention Mechanism

IoT-based Smart Home Security System with Machine Learning Models

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 to feature extraction using the info gain attribute evaluation feature extraction 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 was then formed. These datasets were subjected to performance testing using several machine learning methods and test choices (crossfold-10, 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 high accuracy detection rates are achieved for IoT devices with limited processing power.

Performance Analysis of Facial Image Feature Extraction Algorithm for Smart Home Security System Detection

Alongside the development of technology to facilitate multi-family security, security tools are also being developed. Smart home security is one of the very popular security tools in Indonesian home construction. The tool works automatically in real time and has no restrictions on environmental conditions. However, currently available tools still lack consistent accuracy and consistent performance. To solve this problem, the author proposes a smart home security system with an Arduino UNO-connected camera, two relay modules, a magnetic lock, and connecting to a home Internet of Things system. The methods used in the research for this thesis project were: 1. Literature review of ongoing Smart Home Security using facial image feature extraction algorithm research; 2. Deployment of Arduino UNO, 2 Relay Module, and Solenoid Lock; 3. The feature extraction algorithm used is Wavelet. The proposed method is expected to achieve an accuracy of 80% or more. The experimental results showed that the proposed prototype of this experiment achieved the accuracy of 85.7%. In addition to accuracy, there is also precision rate at 87.94%, recall rate at 87.56%, and f1-score rate at 87.28%

Multi-sensor data fusion framework for energy optimization in smart homes

Advancements in Internet of Energy (IoE) technologies drive the development of several energy efficient frameworks for better energy optimization, economic savings, safety, and security in smart homes. However, certain challenges such as real-time operational data for each micro-moment, proper application of data fusion techniques, and end-to-end computing and deployment architecture prevent the establishment of an effective energy-efficient framework to provide personalized energy-saving recommendations. This work presents energy management for smart spaces (EMSS), the proposed energy efficiency framework implemented in an edge-cloud computing platform that fuses data from heterogeneous data sources (environmental sensors, camera, plug data, etc.) at appropriate data fusion levels and process them to generate actionable, explainable, personalized, and persuasive recommendations at the right moment. The user response to the generated recommendations triggers the actuators to perform respective energy-saving actions and provide more personalized future recommendations. Further, SMARTHome - a data generation framework based on configurable scenario files and a set of software codes was proposed to generate synthetic data with respect to different building types and micro-moments. The functionalities of the EMSS (device and user registration), user dashboard, analytics, and energy-saving recommendations were made accessible to the user through web and mobile applications. The validation analysis of the EMSS was performed by (i) comparative analysis of the machine learning and deep learning algorithms used by the decision engine to generate energy-saving recommendations and (ii) benchmarking of EMSS based on the taxonomy of data fusion-based energy efficiency frameworks for smart homes.

Cost-Optimized Dynamic Access Control Policy Using Blockchain and Machine Learning for Enhanced Security in IoT Smart Homes

The rapid adoption of Internet of Things (IoT) devices in smart homes has led to growing security vulnerabilities, primarily due to the limitations of traditional, static access control mechanisms. This paper presents a novel, dynamic access control policy that leverages the immutable and transparent nature of Blockchain technology, specifically Ethereum, along with machine learning algorithms to enhance security measures. By integrating machine learning algorithms like Support Vector Machines (SVM) and Neural Networks, the proposed system can adapt and respond to changing behavioural patterns and potential threats in real time. Additionally, a caching mechanism implemented on the Ethereum Blockchain is introduced to optimize system performance and reduce latency. Experimental results demonstrate significant improvements in access control security, system efficiency, and adaptability. The findings of this paper not only contribute to the advancement of secure access control policies for IoT smart homes but pave the way for future research in integrating Blockchain and machine learning for robust and scalable IoT security solutions.