Interconnected Devices Research Articles

Physical-Unclonable-Function-Based Lightweight Anonymous Authentication Protocol for Smart Grid

In the Internet of Everything era of Web 3.0, smart grid (SG) technology is also developing towards intelligent interconnection of terminal devices. However, in the smart grid scenario, security issues are particularly prominent, especially the openness of wireless sensor networks. Sensor nodes are vulnerable to attacks and other security threats, which makes confirming the legitimacy of access identity and ensuring the secure transmission of data an urgent problem to be solved. At present, although a variety of authentication schemes for smart grid nodes have been proposed, most of them have problems. For example, some cannot achieve forward security. Therefore, this paper aims to solve this problem and proposes a lightweight anonymous authentication protocol based on physical unclonable functions (PUFs), which can implement mutual authentication and session key agreement between gateway nodes and sensor nodes. Compared to five state-of-the-art schemes in security and performance, the proposed scheme achieves all eight of the listed security requirements with lightweight calculation overhead, communication overhead, and storage overhead.

Low-temperature growth of wafer-scale amorphous boron nitride films with low-dielectric-constant and controllable thicknesses.

Amorphous boron nitride (aBN) films, with extremely low relative dielectric constant (κ) and chemical inertness, are excellent insulation and packaging materials for electronic device interconnection. It is of great significance to prepare the low-κ aBN films with controllable thickness, but there are still some limitations to achieve the goal. In this study, we succeed in growing wafer-scale aBN films with specific thicknesses from 1.2 to 4.0 nm by varying the growth time and temperature. The thickness of the films increases linearly with growth time and the crystallinity of BN films is precisely controlled by the growth temperature. The preferred temperature for aBN films ranges from 200 to 400 °C. Raman spectroscopy, X-ray photoelectron spectroscopy and transmission electron microscope all confirm the amorphous feature. These films are wafer-scale uniformity and have an ultra-flat surface, with excellent thermal stability and corrosion resistance. Particularly, the growth temperature affects the κ value and breakdown voltage of aBN films. The aBN films grown at 200 °C have the lowest κ value of 1.66 at 100 kHz, along with the breakdown field strength of 5.5 MV/cm. We believe that wafer-level aBN films with various thicknesses can bring new opportunities for the development and application of nanoscale electrical devices.

Advancing IoT Security with an Innovative Machine Learning Paradigm for Botnet Attack Detection

INTRODUCTION: In contemporary society, everyday operations are greatly improved by the Internet of Things (IoT), which connects physical devices to provide digital services. IoT technology offers unified services and streamlines activities across various domains, ranging from remote monitoring to sophisticated welfare systems. However, the growing number of IoT devices presents a security concern. Many of these devices are susceptible to exploitation, leading to diverse vulnerabilities. OBJECTIVES: Resource-constrained IoT devices become prime targets for botnet attacks, manifesting in various forms and penetration methods. Despite numerous research efforts introducing multiple approaches for detecting botnet attacks in IoT, existing methods often fail to achieve satisfactory detection rates. METHODS: Additionally, these approaches struggle to comprehensively analyze the diverse communication networks within the expansive realm of IoT devices. This study proposes an innovative machine-learning framework for detecting IoT botnet threats to address these limitations. RESULTS: This conceptual framework exhibits a remarkable capability to identify a spectrum of botnet attacks, showcasing a detection accuracy of 99.5 per cent, significantly surpassing the performance of other prevalent machine-learning approaches. CONCLUSION: Through this research, we aim to enhance the security paradigm of IoT networks, ensuring robust protection against evolving botnet threats in the dynamic landscape of interconnected devices.

A Novel Lucas-based Clustering Optimization for Enhancing Survivability in Smart Home Design

This study presents a novel Lucas-based topology optimization framework to enhance network survivability in smart homes, particularly against random node failures. As the proliferation of interconnected devices in the Internet of Things (IoT) environments increases, so does the vulnerability of these networks to node failures, which can significantly disrupt connectivity and functionality. By integrating the mathematical properties of Lucas numbers with advanced graph theory concepts, specifically the Trimet Graph Optimization (TGO) model, this framework systematically addresses the challenges posed by random node failures. The proposed model optimizes network topologies to ensure robust connectivity and resilience, allowing smart home networks to maintain operational integrity even under adverse conditions. Simulations and theoretical analyses demonstrate the effectiveness of this approach, highlighting its potential to improve the reliability of smart home networks.

Comprehensive Learning Salp Swarm Algorithm with Ensemble Deep Learning-based ECG Signal Classification on Internet of Things Environment

The Internet of Things (IoT) in healthcare relates to implementing interconnected devices and systems for collecting and sharing healthcare information in real time. The integration of IoT in healthcare has the potential to enhance patient outcomes, reduce healthcare costs, and improve the efficacy of medical services. Electrocardiogram (ECG) is a non-invasive heart monitoring method that has become widely accessible due to user-friendly, low-cost, and lead-free wearable heart monitors. However, relying on overworked caregivers for manual monitoring is inefficient. This study develops a Comprehensive Learning Salp Swarm Algorithm with Ensemble Deep Learning (CLSSA-EDL) technique for ECG signal classification in IoT healthcare. The objective of CLSSA-EDL is to detect and classify ECG signals to support decision-making in the IoT healthcare environment. The CLSSA-EDL approach employs the DenseNet201 feature extraction method, with hyperparameters optimally selected by the CLSSA system. For ECG signal detection and classification, an ensemble model using a Stacked Autoencoder (SAE), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM) is utilized. The CLSSA-EDL technique was evaluated on a benchmark ECG dataset, achieving an accuracy of 98.7%, sensitivity of 97.5%, and specificity of 99.1%, demonstrating superior performance compared to recent algorithms.

IoT based Predictive Modeling Techniques for Cancer Detection in Healthcare Systems

Background: The main objective of the Internet of Things (IoT) has significantly influenced and altered technology, such as interconnection, interoperability, and sensor devices. To ensure seamless healthcare facilities, it's essential to use the benefits of ubiquitous IoT services to assist patients by monitoring vital signs and automating functions. In healthcare, the current stateof-the-art equipment cannot detect many cancers early, and almost all humans have lost their lives due to this lethal sickness. Hence, early diagnosis of cancer is a significant difficulty for medical experts and researchers. Methods: The method for identifying cancer, together with machine learning and IOT, yield reliable results. In the Proposed model FCM system, the SVM methodology is reviewed to classify either benign or malignant disease. In addition, we applied a recursive feature selection to identify characteristics from the cancer dataset to boost the classifier system's capabilities. Results: This method is being applied in conjunction with fuzzy cluster-based augmentation, and classification can employ continuous monitoring to forecast lung cancer to improve patient care. In the process of effective image segmentation, the fuzzy-clustering methodology is implemented, which is used for the goal of obtaining transition region data. Conclusion: The Otsu thresholding method is applied to help recover the transition region from a lung cancer image. Furthermore, morphological thinning on the right edge and the segmentationimproving pictures are employed to increase segmentation performance. In future work, we intend to design a prototype to ensure real-time analysis to provide enhanced results. Thus, this work may open doors to carry patent-based outcomes.

Deep Learning Approaches for DDoS Attack Detection in Communication Networks and IoT: A Comprehensive Review

The increasing adoption of transformative technologies, such as the Internet of Things (IoT), has brought convenience and optimization to various domains. However, it has also introduced new challenges, including the vulnerability to Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks. DDoS attacks have shown an alarming rise in frequency and potency, making it crucial to devise efficient mechanisms to prevent such attacks and safeguard communication networks. IoT networks, with their numerous interconnected devices and limited resources, are particularly susceptible to DDoS attacks. Traditional rule-based approaches have proven insufficient to cope with the dynamic nature of modern attacks, leading to the emergence of deep learning-based detection and mitigation techniques. Deep learning models, supported by real-world datasets, off er promising results with detection rates exceeding 98%. This study explores various deep learning architectures, focusing on their success in DDoS attack detection, particularly in IoT networks. It also addresses the challenges associated with such networks and highlights potential areas for future research.

Analyzing and Mitigating Attacks in IoT Smart Home Using a Threat Modeling Approach-Based STRIDE

The Internet of Things (IoT) is a network of interconnected devices that enables data exchange. It is widely used in areas such as healthcare, aviation, agriculture, energy, and home automation. Despite its rapid growth and the massive adoption of connected devices, IoT presents significant security risks. Traditional threat modeling approaches are insufficient to address these risks. Architecture-based modeling is recommended, as it considers the entire system and helps in understanding potential threats. Threat modeling is a systematic technique used to identify and evaluate potential threats that could compromise the security of a system. The main objective is to understand the vulnerabilities of a system in order to design appropriate security measures to mitigate them. This paper aims to analyze and mitigate specific IoT smart home threats using the STRIDE threat modeling framework, which systematically identifies potential vulnerabilities at the development level. By applying STRIDE, which stands for Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, and Elevation of Privilege, we focused on addressing key security threats, including denial of service (DoS), phishing, and man-in-the-middle (MitM) attacks. Our findings demonstrate that the proposed mitigation strategies are effective in countering these threats, providing a robust security layer for IoT smart homes. Through this study, we highlight the importance of architecture-based threat modeling to enhance security within the IoT ecosystem and offer practical solutions that strengthen IoT smart home resilience. The outcomes of the STRIDE-based analysis and the effectiveness of the mitigation techniques are detailed, offering empirical evidence to support our approach.

IoT Network Security Anomaly Detection and Classification using Deep Learning

The Internet of Things (IoT) is an expanding network of interconnected devices exposed to growing cyber security threats. Integrating AI-powered solutions presents a promising avenue for enhancing anomaly detection and classification. This study delves into developing a comprehensive methodology leveraging machine learning and deep learning techniques. Utilizing the BoTNeTIoT-L01 dataset, meticulously curated from IoT devices, the research focuses on data gathering, preprocessing, and exploratory data analysis to unearth underlying patterns and anomalies within network traffic data. Subsequently, a suite of machine learning models, including Logistic Regression, LightGBM (Light Gradient-Boosting Machine), and Decision Tree, along with a deep learning model optimized with the Adam optimizer, is employed to detect and classify anomalies effectively. The comparative analysis underscores the superior performance of advanced models such as LightGBM and Decision Tree, showcasing their efficacy in accurately identifying security threats within IoT environments. The study also addresses pertinent technical challenges, ethical considerations, and future directions, emphasizing the imperative for responsible deployment and ongoing innovation in AI-powered IoT security solutions.

Stacking Ensemble Deep Learning for Real-Time Intrusion Detection in IoMT Environments

The Internet of Medical Things (IoMT) is revolutionizing healthcare by enabling advanced patient care through interconnected medical devices and systems. However, its critical role and sensitive data make it a prime target for cyber threats, requiring the implementation of effective security solutions. This paper presents a novel intrusion detection system (IDS) specifically designed for IoMT networks. The proposed IDS leverages machine learning (ML) and deep learning (DL) techniques, employing a stacking ensemble method to enhance detection accuracy by integrating the strengths of multiple classifiers. To ensure real-time performance, the IDS is implemented within a Kappa Architecture framework, enabling continuous processing of IoMT data streams. The system effectively detects and classifies a wide range of cyberattacks, including ARP spoofing, DoS, Smurf, and Port Scan, achieving an outstanding detection accuracy of 0.991 in binary classification and 0.993 in multi-class classification. This research highlights the potential of combining advanced ML and DL methods with ensemble learning to address the unique cybersecurity challenges of IoMT systems, providing a reliable and scalable solution for safeguarding healthcare services.

Low-Cost Embedded System Applications for Smart Cities

The Internet of Things (IoT) represents a transformative technology that allows interconnected devices to exchange data over the Internet, enabling automation and real-time decision making in a variety of areas. A key aspect of the success of the IoT lies in its integration with low-resource hardware, such as low-cost microprocessors and microcontrollers. These devices, which are affordable and energy efficient, are capable of handling basic tasks such as sensing, processing, and data transmission. Their low cost makes them ideal for IoT applications in low-income communities where the government is often absent. This review aims to present some applications—such as a flood detection system; a monitoring system for analog and digital sensors; an air quality measurement system; a mesh video network for community surveillance; and a real-time fleet management system—that use low-cost hardware such as ESP32, Raspberry Pi, and Arduino, and the MQTT protocol used to implement low-cost monitoring systems applied to improve the quality of life of people in small cities or communities.

Decentralized Identity Management for Internet of Things (IoT) Devices Using IOTA Blockchain Technology

The exponential growth of the Internet of Things (IoT) necessitates robust, scalable, and secure identity management solutions to handle the vast number of interconnected devices. Traditional centralized identity systems are increasingly inadequate due to their vulnerabilities, such as single points of failure, scalability issues, and limited user control over data. This study explores a decentralized identity management model leveraging the IOTA Tangle, a Directed Acyclic Graph (DAG)-based distributed ledger technology, to address these challenges. By integrating Decentralized Identifiers (DIDs), Verifiable Credentials (VCs), and IOTA-specific technologies like IOTA Identity, IOTA Streams, and IOTA Stronghold, we propose a proof-of-concept framework that enhances security, scalability, and privacy in IoT ecosystems. Our implementation on resource-constrained IoT devices demonstrates the feasibility of this approach, highlighting significant improvements in transaction efficiency, real-time data exchange, and cryptographic key management. Furthermore, this research aligns with Web 3.0 principles, emphasizing decentralization, user autonomy, and data sovereignty. The findings suggest that IOTA-based solutions can effectively advance secure and user-centric identity management in IoT, paving the way for broader applications in various domains, including smart cities and healthcare.

Enhancing covert communication in NOMA systems with joint security and covert design.

The explosion of Internet-of-Thing enables several interconnected devices but also gives rise chance for unauthorized parties to compromise sensitive information through wireless communication systems. Covert communication therefore has emerged as a potential candidate for ensuring data privacy in conjunction with physical layer transmission to render two lines of defense. In this paper, we aim to enhance the individual transmission of nearby users in non-orthogonal multiple access (NOMA) systems under scenarios of an eavesdropper who monitors covert transmission before decoding covert information. For this problem, we first provide a comprehensive analysis of the NOMA system in terms of outage probability (OP), secrecy outage probability (SOP), and detection error probability (DEP), where all of them are quantified in exact and asymptotic closed-form expressions. Besides, we have also derived closed-form formulas for users' covert and public rates. Under the system requirements of the maximal OP and SOP and the minimal DEP, we formulate the optimization of resource power allocation to: 1) minimize the OP of covert communication and 2) maximize the covert rate. Thanks to the developed analytical expressions, we obtain closed-form expressions for the sub-optimal power allocation coefficient for each problem. Simulation results validate the efficacy of the analytical mathematical frameworks and reveal that the proposed approaches of power allocation can provide attractive performance improvement compared to fixed power allocations only.

A Survey on Cybersecurity in IoT

The proliferation of the Internet of Things (IoT) has transformed the digital landscape, enabling a vast array of interconnected devices to communicate and share data seamlessly. However, the rapid expansion of IoT networks has also introduced significant cybersecurity challenges. This paper presents a comprehensive survey of cybersecurity in the IoT ecosystem, examining the current state of research, identifying critical security vulnerabilities, and exploring advanced strategies for mitigating threats. The survey covers various facets of IoT security, including device authentication, data integrity, privacy, network security, and the emerging role of artificial intelligence (AI) in bolstering cybersecurity defenses. By synthesizing existing research and highlighting ongoing challenges, this survey aims to provide a holistic understanding of IoT cybersecurity and to guide future research endeavors.

Intrusion Detection System for Internet of Medical Things Using GRU with Attention Mechanism based Hybrid Deep Learning Technique

The proliferation of Internet of Things devices in healthcare, specifically the Internet of Medical Things, has revolutionized patient care and health monitoring systems. Integrating these interconnected medical devices introduces unprecedented security challenges, necessitating robust Intrusion Detection Systems (IDS) to safeguard patient data and healthcare infrastructure. To protect the IoMT devices from numerous malicious attacks, researchers have developed numerous Intrusion Detection Systems, but the development of a practical and real-time IDS remains a challenge. Our proposed IDS addresses this gap and surpasses state-of-the-art IDS techniques for IoMT networks. In this research paper, we have proposed a novel IDS approach for IoMT, leveraging a Hybrid Deep Learning technique to enhance detection accuracy and efficiency. By combining the strengths of the Gated Recurrent Unit (GRU) and Attention Mechanism, the proposed IDS achieves superior performance in detecting anomalous activities in medical networks. The proposed IDS model was evaluated on two publicly available benchmark intrusion datasets and achieved 99.99 % accuracy on the ICU Healthcare Dataset and 98.94 % accuracy on the NF-TON-IoT Dataset. Precision, Recall, F1-Score metrics, and ROC-AUC for the proposed model are promising. To show how effectively the model performed in noisy environments, we also added noise to the features. Moreover, we used the K-Fold Cross Validation Technique to cross-validate the model's performance on both datasets, ensuring the reliability and applicability of the suggested IDS model for IoMT networks.

Convergence of blockchain, IoT, and machine learning: exploring opportunities and challenges – a systematic review

Abstract With the rapid technological advancement, the convergence of the Internet of Things (IoT), blockchain, and machine learning (ML) has a significant revolution across industries, driving them in areas like Industry 4.0, smart cities, and advanced transportation systems. This study presents a comprehensive review of the integration of the IoT, blockchain, and ML technologies, highlighting their transformative impact on diverse industries. It addresses the challenges of scalability, security, and data management posed by the growth of interconnected IoT devices, proposing solutions through advanced algorithms and the integration of blockchain for data security and immutability. By examining real-world applications and reviewing existing literature, this study underscores the potential benefits and complexities of merging these technologies, while also identifying research challenges and future directions. Overall, it offers insights into the innovative constructive collaboration of IoT, blockchain, and ML, which has become the most important of modern technological advances.

Impact analysis of flexible interconnection devices in distribution networks and a setting calculation method for overcurrent protection scheme

AbstractThe integration of flexible interconnection devices (FIDs) into distribution networks introduces complexities in power flow management under both normal and transient/fault conditions, thereby decreasing the selectivity, sensitivity, and reliability of common current protection schemes. This article examines the impact of FIDs on existing current protection arrangements and proposes a novel method for recalculating the overcurrent protection setting. The AC output characteristics of the FID for a short period after AC fault occurrence are first investigated. It is followed by an analysis of FID's impact on the three‐stage current protection scheme in flexible interconnected distribution networks, conducted through theoretical calculations in MATLAB and simulations in PSCAD/EMTDC. A quantitative assessment of the mal‐operation and non‐operation of current protection caused by FIDs of varying capacities, power flow directions, and locations, with a focus on the typical 10 kV 10MVA FID, is also performed. Based on the above findings, a calculation and modification method for Stage III overcurrent protection setting is presented to address mal‐operation issues in FID‐based distribution networks, with its feasibility validated through electromagnetic transient simulations.

Current developments, applications, challenges and future trends in internet of things: A survey

The rapid digitalization in recent years has opened up many technological possibilities, gradually transforming various sectors and society as a whole. This digital shift has enabled advancements in a number of fields, leading to improved resource efficiency, systems and processes. The Internet of Things (IoT) refers to a system of interconnected devices that share information that exchange information with one another via the internet. IoT devices are now everywhere, found in applications ranging from unmanned aerial vehicles to smart home environments, from the Industrial Internet of Things to the Internet of Medical Things. The core concept of IoT revolves around establishing a seamless and intelligent communication ecosystem, facilitating interactions between devices over the internet. This is anticipated to create new opportunities for enhancing services in various societal sectors, such as transportation, farming and smart cities. However, IoT-based networks face limitations and challenges that hinder the realization of their full potential. This paper outlines these challenges and proposes solutions, emphasizing the importance of collaboration and innovation. The paper also anticipates future trends in IoT, particularly the integration of 5G connectivity, cloud computing and AI, and identifies areas for future research to address current challenges and explore new applications.

Deep Learning-Based Intrusion Detection Systems For Network Security in IoT System

The Internet of Things (IoT) has revolutionised various sectors, including healthcare, education, agriculture, and military applications, by enabling seamless communication and data collection among interconnected devices. However, IoT networks' open and decentralised nature exposes them to many security threats and vulnerabilities. Intrusion Detection Systems (IDS) have been developed to address these challenges by identifying and mitigating malicious activities targeting these networks. Despite their importance, many organisations struggle to detect and prevent novel and sophisticated attacks effectively. This paper presents a comprehensive survey of the security issues inherent in IoT environments, emphasising the role of deep learning and machine learning techniques in enhancing IDS capabilities. By analysing existing vulnerabilities and evaluating various methodologies, we highlight the critical need for robust security measures that ensure IoT systems' reliability, privacy, and integrity. Through our findings, we advocate for integrating advanced analytical techniques in IDS to bolster defences against evolving threats in the IoT landscape.

Implementation of Smart Home Automation System

The Internet of Things is a network of physical objects embedded using electronics, sensors, and programmed using software to establish connectivity and provide communication by exchanging data with users, manufacturers, and other IoT devices. Interconnected smart devices have become a ubiquitous part of daily lives. Innovative applications like smart home environments are incorporated into daily life with the increasing popularity of IoT, to improve convenience, comfort, energy efficiency, and safety. This paper presents a smart home automation system. In this work, the proposed smart home is initially simulated using the Tinkercad platform and later verified by hardware analysis. Smart home automation is efficiently achieved using various components with an Arduino Uno as a central controller which is integrated with various sensors (ultrasonic sensors to detect distance, PIR sensors to monitor the human presence, LDR sensors to adjust lighting, and smoke detectors to ensure fire safety) and other components like relays, transistors to manage high and low power devices, LEDs, push buttons to offer user control and feedback, and lights, buzzers, DC motors, servo motors to provide automation and mechanical movements.