Smart Home Network Research Articles

Design of an Energy-Saving System for Smart Home Decoration Based on Situational Awareness

This article analyzes the specific energy-saving requirements for smart home decoration in the current market to determine the overall plan of the entire smart home decoration energy-saving system. Based on situational analysis combined with grounded theory, organize and summarize the original data, and use factor analysis for dimensionality reduction analysis. Combining the multi-level analysis method, experts evaluated and scored the user experience evaluation indicators, obtained the weight assignment of each influencing factor, and constructed a user experience evaluation model for smart home products based on situational theory. After extensive testing, it was found that the ZigBee based smart home network implemented in this article has low energy consumption, overcomes many shortcomings of smart home networks, and has high application value.

A novel methodology for anomaly detection in smart home networks via Fractional Stochastic Gradient Descent

With the rising integration of IoT devices within smart home environments, securing these interconnected systems against unauthorized access and cyber threats has become increasingly critical. This study introduces a novel methodology utilizing an advanced Artificial Neural Network (ANN) frame-work to enhance attack and anomaly detection capabilities within smart home networks. The objective is to develop a robust model that outperforms traditional detection methods and provides high accuracy and low false positive rates in identifying potential security threats. The research employs a pioneering approach to train the ANN by incorporating a Fractional Stochastic Gradient Descent optimizer grounded in Grunwald–Letnikov fractional calculus. This method was chosen for its potential to refine learning processes and improve detection accuracy over standard optimizers. The evaluation of the model was performed using the DS2OS traffic traces dataset, applying precision, sensitivity (recall), and specificity metrics to assess performance. The proposed model demonstrated exceptional performance with an accuracy of 0.9951. It significantly surpassed traditional methods like Logistic Regression and Support Vector Machines. The precision achieved was high, indicating a low rate of false positives, while the sensitivity and specificity values underscore the model’s ability to identify both typical and unconventional behaviours within the network accurately. This study introduces a robust and efficient ANN-based methodology for enhancing security in smart home IoT networks. Using a fractional stochastic gradient descent optimizer has proven effective in improving the model’s accuracy and reliability in detecting anomalies and attacks. The findings suggest significant implications for the future of IoT security, highlighting the potential for broader applications of fractional calculus in machine learning to enhance cybersecurity measures in various domains.

Machine learning-based anomaly detection for smart home networks under adversarial attack

As smart home networks become more widespread and complex, they are capable of providing users with a wide range of applications and services. At the same time, the networks are also vulnerable to attack from malicious adversaries who can take advantage of the weaknesses in the network's devices and protocols. Detection of anomalies is an effective way to identify and mitigate these attacks; however, it requires a high degree of accuracy and reliability. This paper proposes an anomaly detection method based on machine learning (ML) that can provide a robust and reliable solution for the detection of anomalies in smart home networks under adversarial attack. The proposed method uses network traffic data of the UNSW-NB15 and IoT-23 datasets to extract relevant features and trains a supervised classifier to differentiate between normal and abnormal behaviors. To assess the performance and reliability of the proposed method, four types of adversarial attack methods: evasion, poisoning, exploration, and exploitation are implemented. The results of extensive experiments demonstrate that the proposed method is highly accurate and reliable in detecting anomalies, as well as being resilient to a variety of types of attacks with average accuracy of 97.5% and recall of 96%.

Machine learning-based anomaly detection for smart home networks under adversarial attack

As smart home networks become more widespread and complex, they are capable of providing users with a wide range of applications and services. At the same time, the networks are also vulnerable to attack from malicious adversaries who can take advantage of the weaknesses in the network's devices and protocols. Detection of anomalies is an effective way to identify and mitigate these attacks; however, it requires a high degree of accuracy and reliability. This paper proposes an anomaly detection method based on machine learning (ML) that can provide a robust and reliable solution for the detection of anomalies in smart home networks under adversarial attack. The proposed method uses network traffic data of the UNSW-NB15 and IoT-23 datasets to extract relevant features and trains a supervised classifier to differentiate between normal and abnormal behaviors. To assess the performance and reliability of the proposed method, four types of adversarial attack methods: evasion, poisoning, exploration, and exploitation are implemented. The results of extensive experiments demonstrate that the proposed method is highly accurate and reliable in detecting anomalies, as well as being resilient to a variety of types of attacks with average accuracy of 97.5% and recall of 96%.

Combating Evolving Threats: A Signature-Anomaly Based Hybrid Intrusion Detection System for Smart Homes with False Positive Mitigation

Abstract: As people are looking for a more comfortable life, IoT applications are coming to play. Smart home system is one of the most popular IoT applications in the last decade. A smart home network is crucial to function smart home system properly. Cyber attacks on a smart home network can damage a lot. Network intrusion detection and prevention system (NIDPS) is a good solution to protect against Cyber threat in smart home network. This research will implement hybrid NIDPS in smart home network by combining signature based and anomaly-detection based NIDPS. This hybrid NIDPS will prevent known known attack from public internet, local internet and zero-day attack. Also, this system will be able to reduce false positive result and improve signature based NIDPS rules accurately by manual inspection.

Lightweight Privacy Preserving Scheme for IoT based Smart Home

Background: The Internet of Things (IoT) is the interconnection of physical devices, controllers, sensors and actuators that monitor and share data to another end. In a smart home network, users can remotely access and control home appliances/devices via wireless channels. Due to the increasing demand for smart IoT devices, secure communication also becomes the biggest challenge. Hence, a lightweight authentication scheme is required to secure these devices and maintain user privacy. The protocol proposed is secure against different kinds of attacks and as well as is efficient. Methods: The proposed protocol offers mutual authentication using shared session key establishment. The shared session key is established between the smart device and the home gateway, ensuring that the communication between the smart devices, home gateway, and the user is secure and no third party can access the information shared. Results: Informal and formal analysis of the proposed scheme is done using the AVISPA tool. Finally, the results of the proposed scheme also compare with existing security schemes in terms of computation and communication performance cost. The results show that the proposed scheme is more efficient and robust against different types of attacks than the existing protocols. Conclusion: In the upcoming years, there will be a dedicated network system built inside the home so that the user can have access to the home from anywhere. The proposed scheme offers secure communication between the user, the smart home, and different smart devices. The proposed protocol makes sure that security and privacy are maintained since the smart devices lack computation power which makes them vulnerable to different attacks.

SDN-based detection and mitigation of DDoS attacks on smart homes

The adoption of the Internet of Things (IoT) has proliferated across various domains, where everyday objects like refrigerators and washing machines are now equipped with sensors and connected to the internet. Undeniably, the security of such devices, which were not primarily designed for internet connectivity, is of utmost importance but has been largely neglected. In this paper, we propose a framework for the real-time DDoS attack detection and mitigation in SDN-enabled smart home networks. We capture network traffic during regular operations and during DDoS attacks. This captured traffic is used to train several machine learning (ML) models, including Support Vector Machine (SVM), Logistic Regression, Decision Trees, and K-Nearest Neighbors (KNN) algorithms. These trained models are executed as SDN controller applications and subsequently employed for real-time attack detection. While we utilize ML techniques to protect IoT devices, we propose the use of SNORT, a signature-based detection technique, to secure the SDN controller itself. Real-world experiments demonstrate that without SNORT, the SDN controller goes offline shortly after an attack, resulting in a 100% packet loss. Furthermore, we show that ML algorithms can efficiently classify traffic into benign and attack traffic, with the Decision Tree algorithm outperforming others with an accuracy of 99%.

Network traffic classification based on federated semi-supervised learning

Traffic Classification (TC) has been applied to a wide range of applications, from security monitoring to quality of service (QoS) provisioning in network Internet Service Providers (ISPs). In recent years, many researchers have applied Machine Learning (ML) or Deep Learning (DL) to TC, namely AI-TC. However, AI-TC methods face significant challenges, including high data dependency, exhaustively costly traffic labeling, and network subscribers’ privacy. This paper proposes a TC framework for smart home networks using Federated Learning (FL) that protects traffic data privacy by performing local training and inference of TC models. Firstly, we design a DPI-based traffic labeling method on edge home gateways as FL nodes, which enables these nodes to have data labeling capability while protecting data privacy. Then, a semi-supervised TC model based on an autoencoder (AE) is proposed to reduce the dependence of the model on labeled traffic samples. Finally, an XAI-based method is utilized to interpret the model to ensure its explainability. We validate the proposed method on public and real datasets using benchmarking methods. The experimental results show that the method can achieve high performance using a small number of samples while protecting data privacy and improving the model’s credibility. Experimental code can be found in the following url: https://github.com/PrinceXuan12138/HGW-TC-Experimental-code.

Blockchain-Powered Deep Learning for Internet of Things with Cloud-Assisted Secure Smart Home Networks

Blockchain-Powered Deep Learning for Internet of Things with Cloud-Assisted Secure Smart Home Networks

Utilizing Blockchain Technology to Provide Safety for Smart Home Networks

Utilizing Blockchain Technology to Provide Safety for Smart Home Networks

GRU-based digital twin framework for data allocation and storage in IoT-enabled smart home networks

In recent years, the Internet of Things (IoT) devices utilization with Information Communication Technology (ICT) has grown exponentially in various Smart City applications, including Smart Homes, Smart Enterprises, and others. The fusion of IoT, ICT, and Smart Home delivers interactive solutions to reduce costs and resource consumption, enhance performance, and engage people's needs more virtually and proactively. A smart home has numerous advantages with the integration of emerging advanced technologies. Big data, centralization, data and resource allocation, security, and privacy issues persist as challenges in IoT-enabled Smart Home Networks. To address these challenges, in this paper, we propose a GRU-based Digital Twin Framework for Data Allocation in IoT-enabled Smart Home Networks. Data and resource allocation of smart home applications are completed at the virtual twin layer using Gated Recurrent Unit (GRU)-based Digital Twin Networks. Low-priority data is stored and processed at the Macro-based Stations (MBSs), and high-priority data is transferred to the upper (Security) layer for authentication and validation. Blockchain-based distributed networks are utilized for Smart Home Data authentication at the security layer with a Proof of Authentication (PoAh) Consensus Algorithm; Data is stored at the cloud layer after validation. The validation results of the proposed framework demonstrate superior performance as the quantitative analysis with accuracy 0.9412, Root Mean Square Error (RMSE) 0.0588 for IoT-enable Smart Home compared to existing works as LSTM-based Digital Twin network and provide a secure environment in IoT-enabled Smart Home.

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.

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.

Assessment of a mixed reality smart home controller: HoloHome pilot study on healthy adults

This work presents HoloHome, a Mixed Reality application that aims to provide a new means of interaction to control the Smart Home components while providing continuous support for the inhabitants, including older adults and people with limited mobility and cognitive skills. HoloHome integrates several technological paradigms such as Mixed Reality, Internet of Things, and Ambient Assisted Living to maximize the tailored domestic comfort for the Smart Home’s inhabitants while promoting their comfort, safety, and independence in performing daily activities. It is designed and implemented on Microsoft HoloLens, ensuring the connection of the Mixed Reality environment with the distributed network of domestic sensors through the WiFi-enabled microcontrollers to present an innovative method of interaction with the Smart Home and IoT network. Additionally, HoloHome provides unique services and assistive technologies mainly designed for older adults and people with short-term memory deficits to provide continuous assistance, support, and instruction for household activities. This paper conducts the assessment of HoloHome, where the proposed system's usability, cybersickness, and effectiveness are investigated. The usability of HoloHome is validated on a sample of healthy adults to highlight desirable modifications before further assessments of older adults and people with disabilities. This pilot study demonstrated a good level of perceived usability (71.5%) among healthy adults, leading toward the best possible adjustments prior to proposing the system to the more fragile users.

Blockchain and Machine Learning Inspired Secure Smart Home Communication Network.

With the increasing growth rate of smart home devices and their interconnectivity via the Internet of Things (IoT), security threats to the communication network have become a concern. This paper proposes a learning engine for a smart home communication network that utilizes blockchain-based secure communication and a cloud-based data evaluation layer to segregate and rank data on the basis of three broad categories of Transactions (T), namely Smart T, Mod T, and Avoid T. The learning engine utilizes a neural network for the training and classification of the categories that helps the blockchain layer with improvisation in the decision-making process. The contributions of this paper include the application of a secure blockchain layer for user authentication and the generation of a ledger for the communication network; the utilization of the cloud-based data evaluation layer; the enhancement of an SI-based algorithm for training; and the utilization of a neural engine for the precise training and classification of categories. The proposed algorithm outperformed the Fused Real-Time Sequential Deep Extreme Learning Machine (RTS-DELM) system, the data fusion technique, and artificial intelligence Internet of Things technology in providing electronic information engineering and analyzing optimization schemes in terms of the computation complexity, false authentication rate, and qualitative parameters with a lower average computation complexity; in addition, it ensures a secure, efficient smart home communication network to enhance the lifestyle of human beings.

An intelligent context-aware threat detection and response model for smart cyber-physical systems

Smart cities, businesses, workplaces, and even residences have all been converged by the Internet of Things (IoT). The types and characteristics of these devices vary depending on the industry 4.0 and have rapidly increased recently, especially in smart homes. These gadgets can expose users to serious cyber dangers because of a variety of computing constraints and vulnerabilities in the security-by-design concept. The smart home network testbed setup presented in this study is used to evaluate and validate the protection of the smart cyber-physical system. The context-aware threat intelligence and response model identifies the states of the aligned smart devices to distinguish between real-world typical and attack scenarios. It then dynamically writes specific rules for protection against potential cyber threats. The context-aware model is trained on IoT Research and Innovation Lab - Smart Home System (IRIL-SHS) testbed dataset. The labeled dataset is utilized to create a random forest model, which is subsequently used to train and test the context-aware threat intelligence SHS model's effectiveness and performance. Finally, the model's logic is used to gain rules to be included in Suricata signatures and the firewall rulesets for the response system. Significant values of the measuring parameters were found in the results. The presented model can be used for the real-time security of smart home cyber-physical systems and develops a vision of security challenges for Industry 4.0.

Swarmtrust: A swarm optimization-based approach to enhance trustworthiness in smart homes

As smart home networks become increasingly complex and dynamic, maintaining trust between the various devices and services is crucial. Existing trust management approaches face challenges such as high computational overhead and difficulty in selecting optimal parameters. To address these challenges, we propose a new method for trust management in smart home networks based on swarm optimization, a principle of swarm intelligence. Our proposed approach is based on swarm optimization, which is a principle of swarm intelligence that optimizes communication patterns and manages trust values within the network. In SwarmTrust, the nodes in the smart home network are represented by particles, and the positions and velocities of these particles are adjusted to optimize communication patterns and reduce the computational burden on nodes. This optimization is achieved by evaluating the trust values of each node and adjusting the communication patterns accordingly. The proposed method integrates trust management with several distinct parameters and selects the parameters optimally to maintain efficiency. The proposed approach also addresses optimization challenges by monitoring communication patterns and managing trust values in a distributed manner, which reduces the computational burden on nodes and maintains efficiency. To evaluate the effectiveness of our proposed approach, we conducted simulation experiments and compared the performance of our approach with existing methods. Our results showed notable improvements in terms of computational efficiency and optimized computation.

Simulation of IoT Smart Home Network with Decision Making Based on MajorDoMo Platform

The purpose of the article is to model a smart home with decision-making and the use of the MajorDoMo IoT platform instead of a cloud platform. The main features of the article are: modeling of the IoT network for controlling the control unit on the MajorDoMo platform, the use of the MajorDoMo IoT platform in the Windows system for making decisions when controlling household appliances (fan or air conditioner) based on the temperature and humidity values received from a smartphone modeling sensors, transmitting the results of controlled data to the user's home page. The structure of the MajorDoMo platform is given, its functions are described, and commands for decision support are considered. The use of product rules is proposed as a decision-making model. An example of such a description for data regulation by the household appliances is given. The user can log into the IoT network and view changes in temperature and humidity in real time, as well as check the regulation of the equipment. The possibilities of the PHP language for the implementation of the control process are presented, in which the household device begins to adjust the parameters when the environmental monitoring system detects that the corresponding data exceeds the set threshold.

Security Attacks and Countermeasures in Smart Homes

The Internet of Things (IoT) application is visible in all aspects of humans’ day-to-day affairs. The demand for IoT is growing at an unprecedented rate, from wearable wristwatches to autopilot cars. The smart home has also seen significant advancements to improve the quality of lifestyle. However, the security and privacy of IoT devices have become primary concerns as data is shared among intelligent devices and over the internet in a smart home network. There are several attacks - node capturing attack, sniffing attack, malware attack, boot phase attack, etc., which are conducted by adversaries to breach the security of smart homes. The security breach has a negative impact on the tenants' privacy and prevents the availability of smart home services. This article presents smart homes' most common security attacks and mitigation techniques.

ZPA: A Smart Home Privacy Analysis System Based on ZigBee Encrypted Traffic

Currently, the ZigBee protocol is widely used in smart homes and provides convenience to people. However, smart home devices often carry a large amount of real physical world information, which may result in information leakage problems. In this paper, to reveal the privacy security issues existing in ZigBee-based smart home networks, we design a smart home privacy analysis system based on ZigBee-encrypted traffic, called ZPA. ZPA can extract ZigBee data features based on the device’s operating mode and time window and use state-of-the-art machine-learning models to identify the type and status of smart home devices that could leak users’ private information. Through the analysis of 20 different devices from 5 manufacturers, the results show that even if the ZigBee traffic is protected by encryption, the accuracy of the proposed method in device type identification and state inference can reach approximately 93% and 98%, respectively. The types and statuses of devices in smart homes will reveal the user’s activity information to a certain extent. The privacy security of ZigBee-based smart devices still needs to be further strengthened.