Smart Home Network Research Articles

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.

Design and Implementation of a Pilot Model for IoT Smart Home Networks

Internet of Things (IoT) technology is a complementary part of our style of life. IoT technology coexists with existing cellular communication for developing 5G. Both academic researchers and industrial householders pay attention to the benefits of IoT technology. However, the rapid growth of IoT networks suffers from some limitations, such as heavy traffic load, traffic congestion,... <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">etc</i> . In this paper, a parallel distributed smart home architecture is proposed to solve human-being problems and achieve welfare in society. The proposed network has a light traffic load and low traffic congestion to avoid a single-point failure. The architecture of the proposed smart home network involves connectivity, security, registration, protocols, and scenarios to update registration. Also, the paper presents a framework of state diagrams, processes, and algorithms for IoT smart home networks. This framework includes a design and implementation of a pilot model of IoT smart home domains to meet human beneficiaries. The model consists of six practical smart home network domains in both normal and urgent modes to proof of the concept. The pilot model is programmed based on three major aspects: objectives, processes with algorithms, and state diagrams. The novelty of the paper lies in the capability to implement a smart home architecture pilot model using simple development kits. The design and implementation of the practical IoT smart home networks are tested and measured using a CoolTerm sniffer tool. The results of measurement prove that the proposed model achieves light traffic profiles and low average data rates.

Utilising blockchain technology to provide safety for smart home networks

Utilising blockchain technology to provide safety for smart home networks

Energy Efficient Dynamic Symmetric Key Based Protocol for Secure Traffic Exchanges in Smart Homes

Highly sensitive information about people’s social life and daily activities flows in smart home networks. As such, if attackers can manage to capture or even eavesdrop on this information, the privacy of the users can be compromised. The consequences can be far-reaching, such as knowing the status of home occupancy that can then facilitate burglary. To address these challenges, approaches such as data aggregation and signcryption have been utilized. Elliptic curve cryptography, bilinear pairing, asymmetric key cryptosystem, blockchain, and exponential operations are among the most popular techniques deployed to design these security solutions. However, the computational, storage and communication complexities exhibited by the majority of these techniques are too high. This renders these techniques unsuitable for smart home components such as smart switches and sensors. Some of these schemes have centralized architectures, which present some single points of failure. In this paper, symmetric key authentication procedures are presented for smart home networks. The proposed protocol leverages on cryptographic primitives such as one-way hashing and bitwise exclusive-Or operations. The results indicate that this scheme incurs the lowest communication, storage, and computation costs compared to other related state-of-the-art techniques. Empirically, our protocol reduces the communication and computation complexities by 16.7% and 57.7%, respectively. In addition, it provides backward key secrecy, robust mutual authentication, anonymity, forward key secrecy, and unlinkability. Moreover, it can effectively prevent attacks such as impersonation, session hijacking, denial of service, packet replays, man-in-the-middle, and message eavesdropping.

Intelligent Deep Learning for Anomaly-Based Intrusion Detection in IoT Smart Home Networks

The Internet of Things (IoT) is a tremendous network based on connected smart devices. These networks sense and transmit data by using advanced communication standards and technologies. The smart home is one of the areas of IoT networks, where home appliances are connected to the internet and smart grids. However, these networks are at high risk in terms of security violations. Different kinds of attacks have been conducted on these networks where the user lost their data. Intrusion detection systems (IDSs) are used to detect and prevent cyberattacks. These systems are based on machine and deep learning techniques and still suffer from fitting or overfitting issues. This paper proposes a novel solution for anomaly-based intrusion detection for smart home networks. The proposed model addresses overfitting/underfitting issues and ensures high performance in terms of hybridization. The proposed solution uses feature selection and hyperparameter tuning and was tested with an existing dataset. The experimental results indicated a significant increase in performance while minimizing misclassification and other limitations as compared to state-of-the-art solutions.

Blockchain-Based Smart Home Network Security through ML

Abstract Due in large part to the Internet of Things’ (IoT) anticipated enormous scope and extensive implementation, achieving safe and private communications on the IoT is difficult. Recent initiatives have investigated the use of blockchain technology to enable decentralized protection and privacy. Such methods, however, are prohibitive for the bulk of IoT applications due to their high computational and time requirements. We specifically offer a resource-efficient, blockchain-based IoT security and privacy solution in this study. The approach is made achievable by utilizing Deep Extreme Learning Machine in combination with unique computational resource exploitation in a typical IoT context (such as smart houses) (DELM). In the proposed method, the privacy, integrity, and accessibility of the Blockchain based Architecture of Smart Homes are prudently considered while assessing the reliability of the system. The overheads caused this strategy are negligible with respect to the security and privacy benefits, we further underline by presenting simulated findings.

Self-Healing of Semantically Interoperable Smart and Prescriptive Edge Devices in IoT

Smart homes enhance energy efficiency without compromising residents’ comfort. To support smart home deployment and services, an IoT network must be established, while energy-management techniques must be applied to ensure energy efficiency. IoT networks must perpetually operate to ensure constant energy and indoor environmental monitoring. In this paper, an advanced sensor-agnostic plug-n-play prescriptive edge-to-edge IoT network management with micro-services is proposed, supporting also the semantic interoperability of multiple smart edge devices operating in the smart home network. Furthermore, IoT health-monitoring algorithms are applied to inspect network anomalies taking proper healing actions/prescriptions without the need to visit the residency. An autoencoder long short-term memory (AE-LSTM) is selected for detecting problematic situations, improving error prediction to 99.4%. Finally, indicative evaluation results reveal the mitigation of the IoT system breakdowns.

There's No place like home: Understanding users' intentions toward securing internet-of-things (IoT) smart home networks

Modern households are increasingly becoming digitized as they contain numerous Internet-connected networked devices throughout the home. However, this growth in the adoption of smart devices in households comes with the risk of Internet cyber-attacks that seem to be increasing every year. With many individuals working from home due to the COVID-19 pandemic, smart home networks are becoming small extensions of the organizational IT infrastructure. Amidst this backdrop, this behavioral study aims to understand the factors that drive an individual's intention toward securing their home network from cyber-attacks. We draw upon rational choice theory (RCT) and theory of planned behavior (TPB) to derive a model consisting of cognitive and psychological components to explain an individual's intention to secure their smart home network. From a survey of 503 working professionals, our data analysis shows strong support for our research model, and, thus, the hypothesized relationships between the cognitive and psychological factors and individual security intentions. Practical implications of these results for home users, organizations, and researchers are discussed, which will be helpful for organizational IT security managers in planning for organizational security as the line between home and workplace is becoming increasingly blurred.

Modeling IoT Smart Home Network

The purpose of the article is to present the process of modeling the IoT smart home (SH) network, which combines both user needs and efficiency requirements. The use of Alibaba cloud platform, which reduces complexity and development time, reduces costs, was justified in the project of building the IoT SH network. The structure of this platform is given, its main components are considered and an algorithm for its configuration is given. MQTT is used as an access protocol in the IoT SH network to achieve fast and reliable data transmission. Open source code, reliability, simplicity and other characteristics justify the choice of this data transfer protocol. Modeling of the network IoT SH is based on the knowledge gained in the process of practical implementation. First, the online problems of the system are tested, after the system is able to work after modification and debugging of programs, a street lamp is used as an example to create an instance of an IoT SH network on a cloud platform. The process of creating an example of an IoT SH network is described in detail in steps, in which data from a street lamp is transmitted to a cloud platform, processed there, and then displayed on a mobile device. A mobile phone was used to implement two-way interaction, simulate the sensor of the IoT SH network and display the results. The algorithms for configuring the platform, modeling the sensor and creating an object model of the device of the IoT SH network are given. For some modern control systems, this system is compatible and suitable for a larger number of cases, which contributes to the development of intelligent control in the IoT network.

A PLS-HECC-based device authentication and key agreement scheme for smart home networks

IoT devices permeate our society, collect personal data, and support critical infrastructures such as the healthcare. Therefore, there is a critical need for authentication and authorization schemes for IoT devices to meet privacy requirements, such as mutual authentication and user anonymity, as well as robustness against security attacks. In this paper, we propose a device authentication and key agreement scheme for IoT networks. Our proposal takes as a model the scheme proposed by Rezai et al., and combines it with a physical layer security technique and a hyper-elliptic curve cryptosystem . Our results show that not only our authentication scheme provides anonymity, mutual authentication, and efficiency, but it also provides resilience to various attacks, including man-in-the-middle, replay, and de-synchronization attacks. Our comparison shows that our scheme performs better than the state-of-the-art in terms of security properties, while adding a small overhead of ≈ 10 ( ms ) .

Secure device control scheme with blockchain in a smart home

The Internet of Things (IoT) and blockchain technologies characterizing the era of the fourth industrial revolution have enabled smart home networks to support their various systems and services. In a blockchain-based smart-home network environment, all connected IoT devices must be controlled safely and efficiently. Nevertheless, existing block-chain-based smart-home IoT systems pose a delay issue due to the necessary block generation time. In addition, IoT devices installed in smart homes should be able to prevent forgery attacks such as spoofing because they are often directly associated with personal information. In this study, we proposed an enhanced method to control smart home devices safely and efficiently by applying the zero-knowledge proof combined with a blockchain-based IoT system to protect the public keys of home network devices and the communication among them. The proposed model was approximately 10 s faster than the block generation-based model when it communicated three times in rinkeby, which is one of the test networks of Ethereum.

Using manufacturer usage descriptions for IoT network security: An experimental investigation of smart home network devices

The Internet of Things (IoT) has shown significant growth in the past decades. Recently, IoT networks have been subject to cybersecurity threats on multiple fronts. A lack of improvement in IoT infrastructures' cybersecurity may result in challenges with a broad impact, such as DDoS attacks that target global DNS services. This paper reviews existing solutions to mitigate attacks on and from IoT networks. As a specific mitigation approach, we propose the use of a standardized whitelisting method, namely Manufacturer Usage Description (MUD), which provides enhanced explainability over machine learning-based approaches and is complementary to them. For evaluating the use of MUD in IoT networks, we report a case study, which we conducted through traffic analysis of two IoT devices by detecting recognizable and distinctive traffic patterns, which demonstrate the feasibility of MUD-based intrusion prevention.

Certificateless Authentication Scheme Based on Blockchain in Smart Home Network

The traditional centralized intelligent home network security authentication schemes have security problems such as integrity and confidentiality, while the distributed schemes have the problem of delayed authentication. These schemes are not suitable for the intelligent home environment of edge computing. To solve these problems, a certificateless smart home network authentication scheme based on blockchain and certificateless cryptosystem is proposed to realize mutual authentication among users, intelligent terminals (ITs), and intelligent gateways (IGs). The aggregation signature scheme of certificateless identity is introduced into the authentication of intelligent terminals in smart home network. The IG only needs to generate a signature to complete the identity authentication of multiple ITs. Compared with other authentication schemes, the security verification and performance analysis show that the proposed scheme uses less computation overhead and achieves more security features.

A throughput drop estimation model and its application to joint optimization of transmission power, frequency channel, and channel bonding in IEEE 802.11n WLAN for large-scale IoT environments

The concept of Internet of Things (IoT) has been widely studied in smart home networks, smart city networks, smart grid systems, autonomous driving systems, and smart healthcare systems. In IoT, the IEEE 802.11n wireless local-area network (WLAN) is used as a common communication technology due to its flexibility and low cost. Then, the high performance WLAN is required to enhance quality of service (QoS) of large-scale IoT applications connecting a number of devices or sensors allocated in wide areas. WLAN can use the limited number of partially overlapping channels (POCs) at 2.4 GHz band. The WLAN performance can be degraded by interfered signals from other WLANs. Then, to optimize the POC assignment by reducing interferences, we have proposed the throughput drop estimation model for concurrently communicating multiple links under interferences. Unfortunately, the 40 MHz channel bonding (CB) and the 20 MHz non-CB are considered separately, while the transmission power is always fixed to the maximum. In this paper, we study the throughput drop estimation model under coexistence of CB and non-CB while the transmission power is changed. Then, we present its application to the joint optimization of assigning the transmission power, the frequency channel, and the channel bonding to enhance the throughput performance of IEEE 802.11n WLAN. For evaluations, we compare estimated throughputs by the model with measured ones in various network topologies to verify the model accuracy. Then, we apply the model to the joint assignment optimization in them, and confirm the effectiveness through simulations and experiments using the testbed system.

A Formally Validated Authentication Algorithm for Secure Message Forwarding in Smart Home Networks

A Formally Validated Authentication Algorithm for Secure Message Forwarding in Smart Home Networks

Modeling of Intelligent Sensor Duty Cycling for Smart Home Automation

The advancement of wireless sensor networks (WSNs) improves various smart home automation services and home users’ living standards. However, efficiently collecting data and automating smart home services require the extensive deployment of the sensors. Thus, one of the crucial and challenging tasks is to minimize the sensors’ energy consumption for monitoring and automating various activities in a smart home. In this article, we present a solution to control the excessive energy consumption of sensors used to detect various activities of daily living (ADL) of a smart home resident. The sensors within a smart home network are divided into various groups employing the recurrent neural network (RNN) and dynamic time warping (DTW) techniques to predict the activities with high accuracy and less energy consumption. The smart home users’ future activities are forecast with bidirectional long short-term memory (BLSTM) RNN model to select those sensors that are likely to predict the upcoming activities. Similarly, to predict the home users’ unusual activities, a guard sensor is elected among sensors with high similarities with each other using DTW. The sensor’s role is evenly switched between different modes to maintain a fair tradeoff between energy and accuracy. An extensive set of simulations is performed to validate the proposed scheme’s work integrating datasets from authentic sources. Finally, the proposed system significantly reduces the sensors’ energy consumption and prolongs the battery lifetime to approximately 137 days. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This article presented an energy-efficient duty-cycling scheme for automating sensors’ operations in a smart home scenario. The traditional duty-cycling schemes mainly provide solutions based on appointing sentries or predicting smart home users’ next activity using models, such as Bayesian networks. We design a system that integrates the advantages of both sentry and prediction-based schemes to reduce the amount of energy required by sensors to detect and automate smart home users’ activities with high accuracy and precision. The active sensors are appointed using a bidirectional long short-term memory recurrent neural network. Similarly, the guard sensors are assigned to detect unusual activities using the similarities among idle sensors. This study could be used to automate the smart home sensors for detecting home user’s activities with less energy, which ultimately prolongs the battery lifetime of the sensors.

Efficient Security Systems Solutions for Smart Homes

Abstract In this article we aim to make an analysis of the evolution of security systems for smart homes and to present some effective solutions. Manufacturers are not developing smart devices from scratch, but are using existing solutions launched by large IT companies to help build smart home networks. The sensors are integrated in different places in homes, offices, equipment, sometimes even clothes, to detect and monitor the location, actions, necessary signs, room temperature and humidity levels of users properties. However, ensuring confidentiality and effective security is a major challenge for smart homes. The increased risk of data security and information leakage makes smart home security an important challenge that needs to be carefully considered.

Denial of Service (DoS) Defences against Adversarial Attacks in IoT Smart Home Networks using Machine Learning Methods

The availability of information and its integrity and confidentiality are important factors in information and communication of the system security. The DDoS attack generally means Distributed denial of services generates many enormous packets to slow and down the Services for actual users who use services. The study examines the impact of a considerable rise in the number of connected devices in the IoT concept on the quantity and volume of DDoS attacks. Thanks to machine learning-based technology, intrusion Detection Systems (IDS) can be versatile and efficient. However, the advancement of machine learning systems, alongside the application of the uses for Adversarial Machine Learning, has also introduced a new potential attack vector; machine learning models which support the uses of the IDS’ decisions may be subject to cyberattacks known as Adversarial Machine Learning (AML). AML is widely applicable to manipulating data and network traffic that transverse networked devices in the IoT setting. However, harmful network packets are frequently misclassified as benign perturbations in the machine learning classifier’s decision bounds. Because of this, machine learning-based detectors such as malware scanners skip those flaws and reduce the risk of delaying detection and spreading malicious code, and incurring issues such as personal information leaking, damaged hardware, and financial loss. Furthermore, this research investigates which DoS attack techniques should be implemented and how adversary samples should be constructed to strengthen the robustness of supervised models utilizing adversarial training. The system obtained 99.98% accuracy with XGBoost and 99.96% accuracy achieved with the decision tree and AdaBoost.

Blockchain-Based Smart Home Networks Security Empowered with Fused Machine Learning.

Security and privacy in the Internet of Things (IoT) other significant challenges, primarily because of the vast scale and deployment of IoT networks. Blockchain-based solutions support decentralized protection and privacy. In this study, a private blockchain-based smart home network architecture for estimating intrusion detection empowered with a Fused Real-Time Sequential Deep Extreme Learning Machine (RTS-DELM) system model is proposed. This study investigates the methodology of RTS-DELM implemented in blockchain-based smart homes to detect any malicious activity. The approach of data fusion and the decision level fusion technique are also implemented to achieve enhanced accuracy. This study examines the numerous key components and features of the smart home network framework more extensively. The Fused RTS-DELM technique achieves a very significant level of stability with a low error rate for any intrusion activity in smart home networks. The simulation findings indicate that this suggested technique successfully optimizes smart home networks for monitoring and detecting harmful or intrusive activities.

IoDM: A Study on a IoT-Based Organizational Deception Modeling with Adaptive General-Sum Game Competition

Moving target defense (MTD) and decoy strategies, measures of active defense, were introduced to secure both the proactive security and reactive adaptability of internet-of-things (IoT) networks that have been explosively applied to various industries without any strong security measures and to mitigate the side effects of threats. However, the existing MTD and decoy strategies are limited to avoiding the attacker’s reconnaissance and initial intrusion attempts through simple structural mutations or inducing the attackers to a static trap based on the deceptive path and lack approaches to adaptively optimize IoT in consideration of the unique characteristic information by the domain of IoT. Game theory-based and decoy strategies are other options; however, they do not consider the dynamicity and uncertainty of the decision-making stages by the organizational agent related to the IoT domains. Therefore, in this paper, we present a type of organizational deception modeling, namely IoT-based organizational deception modeling (IoDM), which considers both the dynamic topologies and organizational business fingerprints customized in the IoT domain and operational purpose. For this model, we considered the practical scalability of the existing IoT-enabled MTD and decoy concepts and formulated the partially incomplete deceptive decision-making modeling for the cyber-attack and defense competition for IoT in real-time based on the general-sum game. According to our experimental results, the efficiency of the deceptive defense of the IoT defender could be improved by 70% on average while deriving the optimal defense cost compared to the increased defense performance. The findings of this study will improve the deception performances of MTD and decoy strategies by IoT scenarios related to various operational domains such as smart home networks, industrial networks, and medical networks. To the best of our knowledge, this study has employed social-engineering IoT knowledge and general-sum game theory for the first time.