Home Internet Of Things Devices Research Articles

A lightweight group-based SDN-driven encryption protocol for smart home IoT devices

With the increasing adoption rate of Internet of Things (IoT) devices in smart home applications, it is vital to safeguard the privacy and security of information, communications among IoT devices, and the underlying infrastructure. In open communication scenarios, an adversary can easily tamper with data transmitted by IoT communication devices. This paper proposes a softwarized lightweight authentication key agreement scheme for smart home applications in Software-Defined IoT (SDIoT) environment. The proposed scheme comprises registration, authentication, and key selection phases. Devices are registered in the registration phase after the successful completion of validation checks, while sessions’ keys are granted to the registered devices in the authentication phase. In the final phase, controllers classify IoT devices based on pre-defined parameters and impose class-specific access control based on classification. The security of the proposed scheme is validated using the widely accepted AVISPAs verification tool against a strong adversary. Simulation results demonstrate that our proposed scheme outperforms existing schemes in terms of running time, computation complexity, and energy consumption. It is found that the proposed scheme has significant cost-effectiveness.

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.

Digital Forensics for E-IoT Devices in Smart Cities

With the global expansion of urban infrastructure and development of 5G communication technology, advanced information and communications technology has been applied to power systems and the use of smart grids has increased. Smart grid systems collect energy data using Internet-of-Things (IoT) devices, such as data concentrator units (DCUs) and smart meters, to effectively manage energy. Services and functions for energy management are being incorporated into home IoT devices. In this paper, the IoT for energy management in smart cities and smart homes is referred to as the E-IoT. Systems that use the E-IoT can efficiently manage data, but they present many potential security threats, because the E-IoT devices in such homes and enterprises are networked for energy management. Therefore, in this study, to identify vulnerabilities in the E-IoT device systems, digital forensics is applied to the E-IoT device systems. E-IoT devices supplied to Korean power systems were used to build a digital forensic test bed similar to actual E-IoT environments. For digital forensics application, E-IoT data acquisition and analysis methodology was proposed. The proposed methodology consisted of three methods—network packet data analysis, hardware interface analysis, and mobile device paired with E-IoT—which were applied to a DCU, smart meter, smart plug, smart heat controller, smart microwave, and smart monitoring system. On analyzing the user and system data acquired, artifacts such as the device name and energy consumption were derived. User accounts and passwords and energy-usage logs were obtained, indicating the possibility of leakage of personal information and the vulnerabilities of E-IoT devices.

CADeSH: Collaborative Anomaly Detection for Smart Homes

Although home IoT (Internet of Things) devices are typically plain and task oriented, the context of their daily use may affect their traffic patterns. For this reason, anomaly-based intrusion detection systems tend to suffer from a high false positive rate (FPR). To overcome this, we propose a two-step collaborative anomaly detection method which first uses an autoencoder to differentiate frequent (`benign') and infrequent (possibly `malicious') traffic flows. Clustering is then used to analyze only the infrequent flows and classify them as either known ('rare yet benign') or unknown (`malicious'). Our method is collaborative, in that (1) normal behaviors are characterized more robustly, as they take into account a variety of user interactions and network topologies, and (2) several features are computed based on a pool of identical devices rather than just the inspected device. We evaluated our method empirically, using 21 days of real-world traffic data that emanated from eight identical IoT devices deployed on various networks, one of which was located in our controlled lab where we implemented two popular IoT-related cyber-attacks. Our collaborative anomaly detection method achieved a macro-average area under the precision-recall curve of 0.841, an F1 score of 0.929, and an FPR of only 0.014. These promising results were obtained by using labeled traffic data from our lab as the test set, while training the models on the traffic of devices deployed outside the lab, and thus demonstrate a high level of generalizability. In addition to its high generalizability and promising performance, our proposed method also offers benefits such as privacy preservation, resource savings, and model poisoning mitigation. On top of that, as a contribution to the scientific community, our novel dataset is available online.

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.

Augmenting IoT Intrusion Detection System Performance Using Deep Neural燦etwork

Due to their low power consumption and limited computing power, Internet of Things (IoT) devices are difficult to secure. Moreover, the rapid growth of IoT devices in homes increases the risk of cyber-attacks. Intrusion detection systems (IDS) are commonly employed to prevent cyberattacks. These systems detect incoming attacks and instantly notify users to allow for the implementation of appropriate countermeasures. Attempts have been made in the past to detect new attacks using machine learning and deep learning techniques, however, these efforts have been unsuccessful. In this paper, we propose two deep learning models to automatically detect various types of intrusion attacks in IoT networks. Specifically, we experimentally evaluate the use of two Convolutional Neural Networks (CNN) to detect nine distinct types of attacks listed in the NF-UNSW-NB15-v2 dataset. To accomplish this goal, the network stream data were initially converted to two-dimensional images, which were then used to train the neural network models. We also propose two baseline models to demonstrate the performance of the proposed models. Generally, both models achieve high accuracy in detecting the majority of these nine attacks.

"You Just Assume It Is In There, I Guess": Understanding UK Families' Application and Knowledge of Smart Home Cyber Security

The Internet of Things (IoT) is increasingly present in many family homes, yet it is unclear precisely how well families understand the cyber security threats and risks of using such devices, and how possible it is for them to educate themselves on these topics. Using a survey of 553 parents and interviews with 25 families in the UK, we find that families do not consider home IoT devices to be significantly different in terms of threats than more traditional home computers, and believe the major risks to be largely mitigated through consumer protection regulation. As a result, parents focus on teaching being careful with devices to prolong device life use, exposing their families to additional security risks and modeling incorrect security behaviors to their children. This is a risk for the present and also one for the future, as children are not taught about the IoT, and appropriate cyber security management of such devices, at school. We go on to suggest that steps must be taken by manufacturers and governments or appropriate trusted institutions to improve the cyber security knowledge and behaviors of both adults and children in relation to the use of home IoT devices.

Attenuation band impedance matched BRF for measurements of electromagnetic noise

AbstractWireless communication devices such as wireless LANs and RFIDs are essential for smart home appliances and IoT (Internet of Things) devices in Smart Cities. When measuring the high‐frequency noise generated by devices, wireless signals of wireless communication devices and high‐frequency noise are measured at the same time. As a result, spectrum analyzers have insufficient dynamic range, or the excess signal from wireless communication devices causes harmonic distortion of the mixer inside the receiver, resulting in inaccurate measurements. Accordingly, a band rejection filter (BRF) is required to attenuate the radio signal. CISPR 16‐2‐3:2016+A1: 2019 assumes that impedances are matched for measurement, so if the impedance mismatch is large, the expanded uncertainty specified in CISPR 16‐4‐2:2011+A1:2014+A2:2018 increases. In this study, we propose a new BRF with impedance matching in the attenuation range and show its principle. As a result, the extended uncertainty could be reduced to 3.96 dB for the BRF of this study compared to 10.63 dB for the conventional BRF. In addition, the method comparison of CISPR 16‐2‐3:2016+A1: 2019 and vector network analyzer shows that the difference in attenuation of the conventional BRF was about −0.8 to 3.3 dB, whereas the BRF in this study were about −0.2 to 0.3 dB. The reduction of the expanded uncertainty and the good measurement results confirm the high usefulness of the CISPR 16‐2‐3 measurement system for the measurement of weak electromagnetic noise.

Efficient incentive mechanism for video distribution on the home Internet of Things devices

Providing users with nearby video distribution services based on Edge Computing (EC) reduces backbone traffic and improves users' viewing experience. However, the behavior of edge networks is difficult to manage and cannot guarantee the rationality of video resource distribution, which has become a significant challenge for online video services. This paper takes the idle resources from home Internet of Things (IoT) devices for video distribution service as the research point. It proposes an incentive mechanism based on behavior management and balance of resources (BMBR). By paying attention to the rationality of resource distribution and establishing reasonable reward and punishment measures for edge devices, the incentive mechanism makes the initiative of edge devices and the relationship between resource supply and demand reach a stable equilibrium. Simulation results and analysis show that the incentive mechanism can effectively improve the enthusiasm for resource sharing in the edge network, ensure the rationality of resource distribution to reduce the resource price fluctuations, and maintain the balance of resource supply and demand.

Sovereign: Self-Contained Smart Home With Data-Centric Network and Security

Recent years have witnessed the rapid deployment of smart homes; most of them are controlled by remote servers in the cloud. Such designs raise security and privacy concerns for end users. In this article, we describe the design of Sovereign, a home Internet of Things (IoT) system framework that provides end users complete control of their home IoT systems. Sovereign lets home IoT devices and applications communicate via application-named data and secures data directly. This approach enables direct, secure, one-to-one, and one-to-many Device-to-Device communication over wireless broadcast media. Sovereign utilizes semantic names to construct usable security solutions. We implement Sovereign as a publish–subscribe-based development platform together with a prototype home IoT controller. Our preliminary evaluation shows that Sovereign provides a systematic, easy-to-use solution to user-controlled, self-contained smart homes running on existing IoT hardware without imposing noticeable overhead.

Preserving Contextual Privacy for Smart Home IoT Devices With Dynamic Traffic Shaping

Internet of Things (IoT) enables physical devices embedded with sensors, software, and other technologies to interoperate and exchange data with other systems over the Internet. Privacy is a huge concern for IoT devices as personal information is constantly being shared through them. Though the best industrial standards like end-to-end encryption are being followed to ensure content-based privacy, contextual privacy concerns still exist. This study focuses on user activity inference attacks, where a passive network observer can infer the private in-home activity of a user by analyzing encrypted IoT traffic metadata. Most of the previous solutions addressing these attacks have either reduced the usability of the devices, increased data overhead, or failed against packet-level signature-based attack scenarios. This study introduces a new defense mechanism that combines dummy packet generation with dynamic link padding. This process makes it difficult for the adversary to avail contextual information about the state of the device (ON or OFF), along with the temporal information (time of state change) from encrypted IoT traffic metadata. We reverse the packet-level signature-based attacks to get device-specific signatures, which helps us generate dummy traffic for the duration of device-specific signatures. Consequently, this results in increased false positives for device state identifications and low traffic overhead. We simulate a state-of-the-art attack scenario to test and vindicate our solution over existing data sets.

Internet of Things (IoT) Cybersecurity Challenges and Mitigation Mechanisms

This study deployed desktop research methodology in seeking to understand the cybersecurity challenges confronting Internet of Things (IoT) and the mitigation mechanisms. Objects are no longer independent from each other; rather, connectivity through the power of the internet has connected them. IoT is no longer a new invention or innovation in the technology industry and human lives. The concept is widely used in different industries such as healthcare, food, finance, manufacturing, government, insurance, oil and gas, transportation, postal services, defence, and many more. These industries are using the ability of IoT to function optimally in facilitating different activities. Users of technology devices use IoT devices in homes, offices, and other places to increase productivity and efficiency daily. This article analyses the cybersecurity challenges of IoT and its mitigation mechanisms. The findings identified different cybersecurity challenges confronting IoT and the ways to mitigate them.

Riding the IoT Wave With VFuzz: Discovering Security Flaws in Smart Homes

Z-Wave smart home Internet of Things devices are used to save energy, increase comfort, and remotely monitor home activities. In the past, security researchers found Z-Wave device vulnerabilities through reverse engineering, manual audits, and penetration testing. However, they did not fully use fuzzing, which is an automated cost-effective testing technique. Thus, in this paper, we present VFUZZ, a protocol-aware blackbox fuzzing framework for quickly assessing vulnerabilities in Z-Wave devices. VFUZZ assesses the target device capabilities and encryption support to guide seed selection and tests the target for new vulnerability discovery. It uses our field prioritization algorithm (FIPA), which mutates specific Z-Wave frame fields to ensure the validity of the generated test cases. We assessed VFUZZ on a real Z-Wave network consisting of 19 Z-Wave devices ranging from legacy to recent ones, as well as different device types. Our VFUZZ evaluation found 10 distinct security vulnerabilities and seven crashes among the tested devices and yielded six unique common vulnerabilities and exposures (CVE) identifiers related to the Z-Wave chipset.

Anomalous behavior detection-based approach for authenticating smart home system users.

This paper presents Duenna, an authentication framework for smart home systems (SHSs). When using controlling apps (e.g., a smartphone app), Duenna makes sure that only legitimate SHS users are allowed to operate their Internet of things (IoT) devices. Duenna is built upon a behavioral anomaly detection (BAD)-based approach. In particular, we hypothesize that SHS users usually operate their home IoT devices in typical and distinctive patterns. Therefore, users that attempt to operate devices differently from such a regular behavior are considered malicious. Technically, Duenna operates in two modes. In an initialization operation, Duenna first collects and processes the historical cyber and physical activities of an SHS user in addition to the historical states of the SHS itself to build a set of incremental anomaly detection (AD) models. Then, in an interactive operation, the trained AD models are, then, used as a baseline from which anomalous commands (i.e., outliers) are detected and rejected, while regular commands (i.e., targets) are considered legitimate and allowed to be executed. Through an empirical evaluation conducted on real-world data, Duenna exhibits high authentication rates ensuring both security and user experience. The findings obtained from such evaluation show that a user behavior-based approach is a promising security scheme that could be integrated into existing SHS platforms.

The Challenge for Energy Saving in Smart Homes: Exploring the Interest for IoT Devices Acquisition in Romania

The Internet of Things (IoT) is a shift towards a digitally enriched environment that connects smart objects and users, aiming to provide merchants with innovative ways to communicate with customers. Therefore, the aim of this research was to identify the Romanian consumer′s openness to technological autonomy and the degree of acceptance of IoT services and technologies to address the green deal principle of low energy consumption. This article investigated the factors that influence the decision to buy smart IoT devices and customers′ perception regarding the security of the data generated in this process. Based on the Technology Acceptance Model (TAM), this research proposed an alternative model consisting of 18 items measured on a Likert scale in order to identify the factors that contribute to the perceived value of the consumer and the behavioral precursors impacting the decision to purchase IoT products. More and more products have built-in sensors and through the Internet connection generate valuable data from a managerial point of view in relation to the customer. Although these data are expected to be of great value to companies, the way they are used is not always transparent and can affect the purchasing decisions and the behavior of IoT products′ customers. The findings of this paper aimed to better promote Smart Home IoT technologies and devices among Romanian people, making possible the control of consumption and the generation of energy savings.

Smart Home Gateway Based on Integration of Deep Reinforcement Learning and Blockchain Framework

The development of information and communication technology in terms of sensor technologies cause the Internet of Things (IoT) step toward smart homes for prevalent sensing and management of resources. The gateway connections contain various IoT devices in smart homes representing the security based on the centralized structure. To address the security purposes in this system, the blockchain framework is considered a smart home gateway to overcome the possible attacks and apply Deep Reinforcement Learning (DRL). The proposed blockchain-based smart home approach carefully evaluated the reliability and security in terms of accessibility, privacy, and integrity. To overcome traditional centralized architecture, blockchain is employed in the data store and exchange blocks. The data integrity inside and outside of the smart home cause the ability of network members to authenticate. The presented network implemented in the Ethereum blockchain, and the measurements are in terms of security, response time, and accuracy. The experimental results show that the proposed solution contains a better outperform than recent existing works. DRL is a learning-based algorithm which has the most effective aspects of the proposed approach to improve the performance of system based on the right values and combining with blockchain in terms of security of smart home based on the smart devices to overcome sharing and hacking the privacy. We have compared our proposed system with the other state-of-the-art and test this system in two types of datasets as NSL-KDD and KDD-CUP-99. DRL with an accuracy of 96.9% performs higher and has a stronger output compared with Artificial Neural Networks with an accuracy of 80.05% in the second stage, which contains 16% differences in terms of improving the accuracy of smart homes.

Machine Learning Challenges for IoT Device Fingerprints Identification

The dramatic growth of Internet of Things (IoT) devices in recent years increases the IoT networks’ vulnerabilities and introduces new challenges among machine learning (ML) algorithms to detect the networked devices. The creation of a Device Fingerprint (DFP) may depend on extracting the network traffic features related to the device except for the identities assigned to it. In this paper, Device Fingerprints for 20 IoT devices are created by extracting 30 features during startup operation. Wireshark Network Protocol Analyzer is used to collect network traffic of 8 home IoT devices, meanwhile the traffics of the remaining devices are taken from the captures_IoT-Sentinel publicly available dataset. Four supervised machine learning algorithms were applied and tested to detect authorized devices and isolate unknown devices, namely: Support Vector Machine (SVM), Decision Tree (DT), Ensemble Random Forest (RF), and Gradient Boosting Classifier (GBC). Random Forest model and Gradient Boosting Classifier both showed better results of about 98.8% as an average of overall accuracy with less difference comparing with the accuracy of Decision Tree. Voting classifier was applied using the three estimators that resulted in high accuracy (DT, RF, and GBC) and achieving 99.5% as an average of overall accuracy.

A Quantitative Study of DDoS and E-DDoS Attacks on WiFi Smart Home Devices

Internet of Things (IoT) has facilitated the prosperity of smart environments such as smart homes. Meanwhile, WiFi is a broadly used technology for the wireless connectivity of IoT devices. However, smart home IoT devices are often vulnerable to various security attacks. This article quantifies the impact of distributed denial of service (DDoS) and energy-oriented DDoS attacks (E-DDoS) on WiFi smart home devices and explores the underlying reasons from the perspective of attacker, victim device, and access point (AP). Compared to the existing work, which primarily focus on DDoS attacks launched by compromised IoT devices against servers, our work focuses on the connectivity and energy consumption of IoT devices when under attack. Our key findings are threefold. First, the minimum DDoS attack rate causing service disruptions varies significantly among different IoT smart home devices, and buffer overflow within the victim device is validated as critical. Second, the group key updating process of WiFi may facilitate DDoS attacks by causing faster victim disconnections. Third, a higher E-DDoS attack rate sent by the attacker may not necessarily lead to a victim's higher energy consumption. Our study reveals the communication protocols, attack rates, payload sizes, and victim devices' ports state as the vital factors to determine the energy consumption of victim devices. These findings facilitate a thorough understanding of IoT devices' potential vulnerabilities within a smart home environment and pave solid foundations for future studies on defense solutions.

G2F: A Secure User Authentication for Rapid Smart Home IoT Management

Internet-of-Things (IoT) devices are widely deployed nowadays. A large number of smart home IoT devices are hosted on a cloud server for easy management. Users can use their accounts to initiate operations and management on IoT devices through a cloud server, such as updating firmware and configuring devices. However, the cloud account may be hacked resulting in adversarial attacks to the hosted IoT devices. As a consequence, an adversary may perform malicious operations through the cloud remotely to the hosted IoT devices without user awareness. Motivated by this, in this article we propose gateway-based 2 factor authentication (G2F), a secure user authentication framework dedicated for a gateway based on the universal 2nd factor (U2F) protocol to enhance the security of IoT devices management. In G2F, the user authentication on the gateway is completed utilizing a hardware token that interacts with the local gateway node to guarantee the token owner’s presence. Furthermore, G2F can grant multiple simultaneous operations on IoT devices through just one user authentication. We implement a prototype to further evaluate the performance of G2F. Based on our realization on the commercial IoT server, i.e., Alibaba Cloud, G2F demonstrates the ability to protect against malicious attacks with high authentication efficiency.

Investigation of Smart Home Security and Privacy: Consumer Perception in Saudi Arabia

One of the fastest and most developing technologies around the globe is the Internet of things (IoT). The research questions in this study focus on the security and privacy challenges for a smart home environment. The geographical region of Saudi Arabia is the selected boundary for the study. The study is focused on finding the problems associated with the Smart Home adaption in Saudi Arabia. However, there is a large phase shift, which is seen towards the increase of threats in smart homes. It is believed that the awareness by humans towards the use of these devices. The level of security offered by the devices, is one of the factors for these threats and privacy issues. This research targets to identify all the facts that can be discarded towards adaption of Smart Homes. It is desirable that a quantitative methodology must be implemented for identification of the population under threat due to IoT devices in smart homes. The views of the users are the major input values to trace the problems. The expected results from this research will provide all the factors which can be improved and provided with proper solution to avoid any security or privacy threats in the Saudi Arabian realm.