Smart Home Internet Of Things Research Articles

IoT Attack Detection using Machine Learning and Deep Learning in Smart Home

The Internet of Things (IoT) has revolutionized the traditional Internet, pushing past its former boundaries by implementing smart linked gadgets. The IoT is steadily becoming a staple of everyday life, having been implemented into various diverse applications, such as cities, smart homes, and transportation. However, despite the technological advancements that the IoT brings, various new security risks have also been introduced due to the development of new types of attacks. This prevents current intelligent IoT applications from adaptively learning from other intelligent IoT applications, which leaves them in a volatile state. In this paper, we conducted a structured literature review (SLR) on Smart Home's IoT attack detection using machine learning and deep learning. Four journal databases were used to perform this review: IEEE, Science Direct, Association for Computing Machinery (ACM), and SpringerLink. Sixty articles were selected and studied, where we noted the various patterns and techniques present in the framework of the selected research. We also took note of the different machine learning and deep learning methods, the types of attacks, and the Network layers present in Smart Home. By conducting an SLR, we analyzed the numerous techniques of IoT attack detection for smart homes proposed by various theoretical studies. We enhanced the studied literature by proposing a new solution for better IoT attack detection in smart homes.

Evaluation of Smart Home Platform Based on Blockchain

A smart home consists of various sensors and actuators that can communicate with each other. In addition, various home appliances have similar capabilities. Therefore, the smart home became one of the applications of the Internet of Things (IoT). In this application, arise the challenges caused by the integration of various devices that came from various vendors have a different data format and keeps the data's security. Blockchain has been proposed as one solution to address these challenges. In this paper, we investigate and evaluate the performance of the blockchain-based solution for smart home IoT. Our experiment results show that the Blockchain can secure the IoT-based smart home, but it took47% longer for packet delivery.

A fuzzy description logic based IoT framework: Formal verification and end user programming.

The Internet of Things (IoT) has become one of the most popular technologies in recent years. Advances in computing capabilities, hardware accessibility, and wireless connectivity make possible communication between people, processes, and devices for all kinds of applications and industries. However, the deployment of this technology is confined almost entirely to tech companies, leaving end users with only access to specific functionalities. This paper presents a framework that allows users with no technical knowledge to build their own IoT applications according to their needs. To this end, a framework consisting of two building blocks is presented. A friendly interface block lets users tell the system what to do using simple operating rules such as "if the temperature is cold, turn on the heater." On the other hand, a fuzzy logic reasoner block built by experts translates the ambiguity of human language to specific actions to the actuators, such as "call the police." The proposed system can also detect and inform the user if the inserted rules have inconsistencies in real time. Moreover, a formal model is introduced, based on fuzzy description logic, for the consistency of IoT systems. Finally, this paper presents various experiments using a fuzzy logic reasoner to show the viability of the proposed framework using a smart-home IoT security system as an example.

Security and Privacy Concerns in the Adoption of IoT Smart Homes: A User-Centric Analysis

The advancement of Internet of Things (IoT) technologies has ushered in a new era of smart homes, promising convenience and automation. However, alongside these advancements, concerns regarding the security and privacy of Internet of Things smart homes have garnered significant attention. The study embarked on a user-centric analysis, delving into the intricacies of security and privacy concerns in the adoption of Internet of Things smart homes. The primary purpose of this research was to investigate the security and privacy concerns that users harbour when adopting Internet of Things smart home technologies. We used SMART-PLS version (4.0.9.6) as the data analysis tool, to examine the concerns and to gain a comprehensive understanding of their impact on adoption. The analysis was, rooted in quantitative research design and based on data gathered through an online questionnaire distributed to the target population of 325 participants. The research response rate was 92%. The hypotheses examined unveiled statistically significant relationships, culminating in results indicating an R<sup>2</sup> of 0.762. This implies that approximately 76.2% of the rationale behind individuals' decisions to either adopt or refrain from using IoT smart home devices, with a focus on security and privacy considerations, can be elucidated by our proposed Structural Equation Model. his model served as a comprehensive lens through which we dissected the intricate interplay of variables shaping user attitudes and behaviors. The study sheds light on the critical concerns of security and privacy within the IoT smart home domain. By leveraging quantitative analysis and a well-crafted Structural Equation Model, we offer valuable insights into the factors influencing user adoption decisions. The research contributes to the broader discourse IoT technology adoption and serves as a foundation for future studies and policy considerations in the ever-evolving landscape of smart homes.

IoT based security and privacy implementation in smart home

IoT based security and privacy implementation in smart home

The effect of technological context on smart home adoption in Jordan

<p><span lang="EN-MY">This research examines the use of internet of things (IoT) smart home technologies in Jordan, using the technologies-organization-environment (TOE) framework and social exchange theory (SET). This study investigates the influence of security and privacy considerations on the behavioral intention to embrace IoT smart home solutions. The research examines the function of trust (TR) in service providers as a mediator and investigates how information technology (IT) knowledge acts as a moderator between technological elements and behavioral intention. Analyzed using smart partial least squares (PLS), data from 315 responses of executives in both listed and non-listed businesses were examined. The results highlight the need of giving priority to technological factors and building TR in service providers to ensure the effective implementation of IoT smart home technology in Jordan.</span></p>

Privacy in Smart Homes with Remote User Authenticated Key Establishment Protocol

The Internet of Things (IoT) is a set of devices that contain electronic software, sensors, and actuators and exchange information using the Internet or network. Because of multiple applications, the number of IoT devices and sensors is increasing daily. Therefore, the traditional addressing security protocols cannot guarantee performance in all scenarios. The most crucial factor is the requirement of a unique addressing and secure scheme that addresses each smart IoT device or application and ensures secure communication. Therefore, robust and lightweight authentication protocols are crucial to ensuring security in smart home IoT (SH-IoT) networks. The proposed scheme provides a remote authentication protocol for the secure exchange of information between the user and the smart home IoT network. Informal and formal security analyses using the ROR model are presented in this work. The presented scheme is validated using the AVISPA tool.

Classification of cyber threats for internet of things

Smart home consists of various Internet of things (IoT) devices. These IoT devices are designed to help and simplify people’s lives. The technical progress of the IoT field is aimed at simplifying human life, thereby creating new cyber threats. Different scientific papers are mentioned that number of IoT devices is growing constantly by 15% per year. As a result, around 1.6 billion IoT devices will be used globally over the internet. It means that IoT devices will be accessed over internet by consumers. Nowadays, Internet is accessible easily by everyone, so they can afford freely the ecosystem of IoT devices at home. Within this development of IoT ecosystem, consumers can face serious problems of transmission and storage of information by IoT devices. These problems might be data theft from IoT devices, using such IoT devices for Denial of Service (DoS) attacks, user tracking and so on. Local cyber threats provide an opportunity for an attacker to gain access to a home network and take advantages of it. Global cyber threats are dangerous because IoT devices can be controlled remotely from anywhere in the world without the knowledge of the user. One of the risks is that the user’s home network of IoT devices could be controlled by botnets to carry out cyber-attacks. The article describes and analyzes current threats to IoT smart home devices and provides examples of data collected and processed by smart devices. Collecting information about users through IoT devices is a novelty of this work.

PiGateway: Real-time granular analysis of smart home network traffic using P4

The Internet of Things (IoT) makes the home smarter by real-time sensing of the situation and transmission of data to the cloud. As IoT devices continue to expand rapidly, a critical need arises for effective device-type identification methods. Accurate identification of IoT device types is essential for various applications, including network management, security, etc. To identify the type of IoT devices, existing works have proposed various Media Access Control (MAC)-based and Machine Learning (ML) approaches. These solutions lack real-time granular analysis and In-network classification of IoT traffic. In this work, we propose PiGateway: a P4-based gateway to classify the type of IoT devices at a line rate and prioritize the device type. Initially, PiGateway develops a Decision Tree (DT) model and generates flow rules from the model. The developed model is deployed in the PiGateway using two mechanisms. One mechanism is to convert the DT model into Match-Action Tables (MATs), where the tables store the flow rules. Another approach developed an optimized model by deploying the DT model into PiGateway as a mathematical expression. Further, to achieve the In-network classification, the gateway calculates flow-level features dynamically from incoming IoT traffic flow. The feature values are given as input to the deployed DT model for classifying the flow into a specific device type. Subsequently, the flow is prioritized based on device type and Type of Service (ToS) field value. Additionally, our proposed solution is designed and tested in a real-time environment. Through implementation results, we show that the PiGateway can classify the type of IoT devices with 98.61% accuracy and an average CPU usage of 10.68%. Also, the classification time in PiGateway is 0.098ms for 8000 flows using an optimized model. Finally, the proposed system serves as a gateway in a smart home IoT network.

An approach towards demand response optimization at the edge in smart energy systems using local clouds

The fourth and fifth industrial revolutions (Industry 4.0 and Industry 5.0) have driven significant advances in digitalization and integration of advanced technologies, emphasizing the need for sustainable solutions. Smart Energy Systems (SESs) have emerged as crucial tools for addressing climate change, integrating smart grids and smart homes/buildings to improve energy infrastructure. To achieve a robust and sustainable SES, stakeholders must collaborate efficiently through an energy management framework based on the Internet of Things (IoT). Demand Response (DR) is key to balancing energy demands and costs. This research proposes an edge-based automation cloud solution, utilizing Eclipse Arrowhead local clouds, which are based on Service-Oriented Architecture that promotes the integration of stakeholders. This novel solution guarantees secure, low-latency communication among various smart home and industrial IoT technologies. The study also introduces a theoretical framework that employs AI at the edge to create environment profiles for smart buildings, optimizing DR and ensuring human comfort. By focusing on room-level optimization, the research aims to improve the overall efficiency of SESs and foster sustainable energy practices.

User experiences with simulated cyber-physical attacks on smart home IoT

With the Internet of Things (IoT) becoming increasingly prevalent in people’s homes, new threats to residents are emerging such as the cyber-physical attack, i.e. a cyber-attack with physical consequences. In this study, we aimed to gain insights into how people experience and respond to cyber-physical attacks to their IoT devices. We conducted a naturalistic field experiment and provided 9 Dutch and 7 UK households, totalling 18 and 13 participants respectively, with a number of smart devices for use in their home. After a period of adaptation, simulated attacks were conducted, leading to events of varying noticeability (e.g., the light going on or off once or several times). After informing people simulated attacks had occurred, the attacks were repeated one more time. User experiences were collected through interviews and analysed with thematic analyses. Four relevant themes were identified, namely (1) the awareness of and concern about privacy and security risks was rather low, (2) the simulated attacks made little impression on the participants, (3) the participants had difficulties with correctly recognizing simulated attacks, and (4) when informed about simulated attacks taking place; participants noticed more simulated attacks and presented decision rules for them (but still were not able to identify and distinguish them well—see Theme 3). The findings emphasise the need for training interventions and an intrusion detection system to increase detection of cyber-physical attacks.

Reliable and Secure Data Transfer in IoT Networks using Knight-Tour and PHLSB Method

Data security has recently become the most pressing problem for corporate owners and even regular people due to the continual development in the speed of data transmission over the entire Internet, and stricter regulations have been made for data protection. Secure key management is now essential for securing information exchange due to the Internet of Things (IoT) and the rapidly advancing mobile device technologies. Smart home and healthcare IoT apps, for instance, offer automated services to users with little user involvement. Data transmission delays and intrusions can affect existing security solutions that use a single link. In this paper, we propose a novel distributed key management scheme for IoT ecosystem. The methods Knight-Tour and polynomial-based hash least significant bit (PHLSB) that take use of flaws in the human visual system (HVS) are combined to form a hybrid approach to image steganography in this research. The Knight's trip path is a chess board layout that follows the route of a horse without stopping at any of the nodes again. In order to create the scrambled image, this travel path pattern is employed to permute the original image's pixel positions. Prior to being masked behind a cover image, this technique makes sure the communication has been encrypted. A strong mathematical tool is a polynomial equation. The cover object's data was hidden using these equations as a secret key. The suggested solution efficiently secures IoT devices by assigning the resource-intensive encryption processing to local organisations. As a result of the proposed framework's simulation findings, network lifetime PDR (Packet Drop Ratio) has improved, throughput has increased, energy consumption has decreased, and latency has decreased.

Sistem Monitoring dan Kontrol Rumah Pintar Berbasis Internet Of Things Untuk Peningkatan Efisiensi Energi

The urgency of research with the theme of designing and implementing a smart home monitoring and control system based on the Internet of Things (IoT) to improve energy efficiency and quality of life is very important to carry out, in general it can be described as being able to increase energy efficiency; improve the quality of life where the system can regulate temperature, humidity and lighting to create a comfortable and healthy environment; encourage innovation; providing solutions to environmental challenges; optimizing energy availability where the smart home IoT system can regulate the use of available energy. Thus, it is very important to carry out this research to face environmental challenges and improve people's quality of life. The main objective of this research is to develop a system that can monitor and control energy use in the home efficiently, so as to improve energy efficiency and the quality of life of home residents, to overcome weaknesses and develop a holistic and effective smart home monitoring and control system. IoT based. The method used in this research is the Research and Development method, where this method is a process or steps to develop a new product or improve an existing product, which can be accounted for. The research results show that the IoT system can be designed and function well based on the results of the tests that have been carried out.

A Systematic Comparison between the Ethereum and Hyperledger Fabric Blockchain Platforms for Attribute-Based Access Control in Smart Home IoT Environments.

Despite the lack of blockchain systems being utilized in modern IoT environments, the prevalence of blockchain technology is increasing, due to its high level of security and accountability. The integration of blockchain technology and access control in a decentralized system for smart home networks is a promising solution to this issue. This paper compares the implementation of attribute-based access control (ABAC) with two popular blockchain platforms, Ethereum and Hyperledger Fabric, for a smart home internet of things (IoT) environment. We present a comprehensive summary of access-control and blockchain-access-control methods, to provide the necessary background for this study. Additionally, we present an original ABAC smart contract for Ethereum, and the modification of a pre-existing Hyperledger Fabric ABAC smart contract, for this comparison. Through the simulation of both implementations, the advantages and limitations will be considered, to determine which is better suited for a smart home IoT environment.

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.

Securing a Smart Home with a Transformer-Based IoT Intrusion Detection System

Machine learning (ML)-based Network Intrusion Detection Systems (NIDSs) can classify each network’s flow behavior as benign or malicious by detecting heterogeneous features, including both categorical and numerical features. However, the present ML-based NIDSs are deemed insufficient in terms of their ability to generalize, particularly in changing network environments such as the Internet of Things (IoT)-based smart home. Although IoT devices add so much to home comforts, they also introduce potential risks and vulnerabilities. Recently, many NIDS studies on other IoT scenarios, such as the Internet of Vehicles (IoV) and smart cities, focus on utilizing the telemetry data of IoT devices for IoT intrusion detection. Because when IoT devices are under attack, their abnormal telemetry data values can reflect the anomaly state of those devices. Those telemetry data-based IoT NIDS methods detect intrusion events from a different view, focusing on the attack impact, from the traditional network traffic-based NIDS, which focuses on analyzing attack behavior. The telemetry data-based NIDS is more suitable for IoT devices without built-in security mechanisms. Considering the smart home IoT scenario, which has a smaller scope and a limited number of IoT devices compared to other IoT scenarios, both NIDS views can work independently. This motivated us to propose a novel ML-based NIDS to combine the network traffic-based and telemetry data-based NIDS together. In this paper, we propose a Transformer-based IoT NIDS method to learn the behaviors and effects of attacks from different types of data that are generated in the heterogeneous IoT environment. The proposed method utilizes a self-attention mechanism to learn contextual embeddings for input network features. Based on the contextual embeddings, our method can solve the feature set challenge, including both continuous and categorical features. Our method is the first to utilize both network traffic data and IoT sensors’ telemetry data at the same time for intrusion detection. Experiments reveal the effectiveness of our method on a realistic network traffic intrusion detection dataset named ToN_IoT, with an accuracy of 97.95% for binary classification and 95.78% for multiple classifications on pure network data. With the extra IoT information, the performance of our method has been improved to 98.39% and 97.06%, respectively. A comparative study with existing works shows that our method can achieve state-of-the-art performance on the ToN_IoT dataset.

Toward a Secure Smart-Home IoT Access Control Scheme Based on Home Registration Approach

The extensive application of the Internet of Things (IoT) and artificial intelligence technology has greatly promoted the construction and development of smart cities. Smart home as the foundation of smart cities can optimize home lifestyles. However, users access the smart home system through public channels, and the transmitted information is vulnerable to attack by attackers, and the privacy and data security of the home user will be difficult to be guaranteed. Therefore, how to protect users’ data and privacy security becomes critical. In this paper, we design a provably secure authentication scheme for the smart home environment, which ensures that only legitimate users can use smart devices. We use the informal model to verify the security of the scheme and formally analyze the security and correctness of the scheme through the Real or Random model. Finally, through the comparison of security and performance analysis, it is proven that our scheme has higher security under similar performance.

Telegram Application for Monitoring, Controlling and Protecting the Consumption of Household Electrical Appliances

Nominal electricity rates always increase, so it is necessary to take steps to save electricity consumption. One of the steps to save electricity consumption is taken by implementing a remote electrical equipment control system based on the Smart Home Internet of Things (IoT) system. Besides being able to save money, IoT systems are also able to help simplify work and human activities. This research aims to implement Internet of Things (IoT) equipment to monitor, control, protect, and On/Off timers for household electrical appliances monitored by the Telegram application and 16×2 LCD. Monitored system parameters i.e. voltage, current, frequency, power, energy, and electricity rates for each device. Device hardware i.e. Arduino Uno, 1 Channel Relay, PZEM-004t Voltage Sensor, NodeMCU ESP8266, and Current Sensor (Current Transformer). The equipment has been successfully tested on 10 different household appliances i.e. charged cellphones, fans, charged laptops, solder, iron, fluorescent lamp 36 W, television (TV), set-top box (STB)-TV, printer, and 5 W bulb lamp. The results showed that the prototype is capable of monitoring, controlling, protecting, and On/Off timer settings as well as being able to be monitored from home electric appliances both near and far using the 16×2 LCD and the Telegram application, respectively. The sensor tool using the Telegram application is able to measure/monitor the parameters of voltage, current and frequency at 10 loads with an average error value of 0.32%, 12.83% and 0.1% respectively compared to measurements using a Multimeter. The IoT prototype is also able to provide more complete, faster (4-5 sec) monitoring, control, protection, timer On/Off parameters and superior performance compared to devices designed by a number of previous researchers.

Studying users’ perception of IoT mobile companion apps

Internet of Things (IoT) products provide over-the-net capabilities such as remote activation, monitoring, and notifications. An associated mobile app is often provided for more convenient usage of these capabilities. The perceived quality of these companion apps can impact the success of the IoT product. We investigate the perceived quality and prominent issues of smart-home IoT mobile companion apps with the aim of deriving insights to: (i) provide guidance to end users interested in adopting IoT products; (ii) inform companion app developers and IoT producers about characteristics frequently criticized by users; (iii) highlight open research directions. We employ a mixed-methods approach, analyzing both quantitative and qualitative data. We assess the perceived quality of companion apps by quantitatively analyzing the star rating and the sentiment of 1,347,799 Android and 48,498 iOS user reviews. We identify the prominent issues that afflict companion apps by performing a qualitative manual analysis of 1,000 sampled reviews. Our analysis shows that users’ judgment has not improved over the years. A variety of functional and non-functional issues persist, such as difficulties in pairing with the device, software flakiness, poor user interfaces, and presence of issues of a socio-technical impact. Our study highlights several aspects of companion apps that require improvement in order to meet user expectations and identifies future directions.

IoT Capabilities Analysis by Using Optimized Machine Learning with Uncertain Traffic Modeling

The Internet of Things (IoT) incorporates software-based physical devices with sensors which require interconnectivity to communicate through the internet and be able to exchange data. IoT traffic can be thought of as the aggregate of packets created by various devices in various contexts, such as smart homes or smart cities. These settings contain several sensors, each of which is dedicated to a certain duty, such as monitoring schemes or gathering cyber-physical information. Thus, unlike internet traffic, which has some human-centric features, IoT traffic and sensor devices consistently perform different tasks and make uncertain quantities of data on a regular basis. The proposed method is based on calculating the entropy values of various uncertain traffic aspects. We describe a novel scheme for identifying IoT devices based on uncertain traffic entropy in this paper. The testbed consists of five popular Smart Home IoT devices with a Raspberry Pi acting as a gateway. Machine learning (ML) algorithms such as support vector machine (SVM), [Formula: see text]-nearest neighbor (KNN) and relevance vector machines (RVM) classifier are used to classify the devices which produce IoT traffic based on uncertain entropy significance. The learning rate of the classifier model is improved by using the flower pollination optimization (FPO) technique to increase the classifier performance. The suggested approach minimizes the amount of time spent on optimization operations while maintaining the predictive performance of the induced uncertain models. We work out the entropy standards of traffic characteristics and categorize the uncertain traffic using ML techniques. Our technique successfully identifies devices in a variety of uncertain network situations, with consistent performance in all scenarios. Our technique is also resistant to unpredictability in network behavior, with abnormalities or uncertainties propagating throughout the network.