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  • Open Access

    ARTICLE

    IWTW: A Framework for IoWT Cyber Threat Analysis

    GyuHyun Jeon1, Hojun Jin1, Ju Hyeon Lee1, Seungho Jeon2, Jung Taek Seo2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1575-1622, 2024, DOI:10.32604/cmes.2024.053465 - 27 September 2024

    Abstract The Internet of Wearable Things (IoWT) or Wearable Internet of Things (WIoT) is a new paradigm that combines IoT and wearable technology. Advances in IoT technology have enabled the miniaturization of sensors embedded in wearable devices and the ability to communicate data and access real-time information over low-power mobile networks. IoWT devices are highly interdependent with mobile devices. However, due to their limited processing power and bandwidth, IoWT devices are vulnerable to cyberattacks due to their low level of security. Threat modeling and frameworks for analyzing cyber threats against existing IoT or low-power protocols have… More >

  • Open Access

    ARTICLE

    The Machine Learning Ensemble for Analyzing Internet of Things Networks: Botnet Detection and Device Identification

    Seung-Ju Han, Seong-Su Yoon, Ieck-Chae Euom*

    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1495-1518, 2024, DOI:10.32604/cmes.2024.053457 - 27 September 2024

    Abstract The rapid proliferation of Internet of Things (IoT) technology has facilitated automation across various sectors. Nevertheless, this advancement has also resulted in a notable surge in cyberattacks, notably botnets. As a result, research on network analysis has become vital. Machine learning-based techniques for network analysis provide a more extensive and adaptable approach in comparison to traditional rule-based methods. In this paper, we propose a framework for analyzing communications between IoT devices using supervised learning and ensemble techniques and present experimental results that validate the efficacy of the proposed framework. The results indicate that using the More >

  • Open Access

    ARTICLE

    Balancing the load and scheduling the tasks using zebra optimizer in IoT based cloud computing for big-data applications

    V. Vijayaraj1, M. Balamurugan1, Monisha Oberoi2

    Revista Internacional de Métodos Numéricos para Cálculo y Diseño en Ingeniería, Vol.40, No.2, pp. 1-11, 2024, DOI:10.23967/j.rimni.2024.05.009 - 31 May 2024

    Abstract Task scheduling is one of the major problems with Internet of Things (IoT) cloud computing. The need for cloud storage has skyrocketed due to recent advancements in IoT-based technology. Sophisticated planning approaches are needed to load the IoT services onto cloud resources professionally while meeting application necessities. This is significant because, in order to optimise resource utilisation and reduce waiting times, several procedures must be properly configured on various virtual machines. Because of the diverse nature of IoT, scheduling various IoT application activities in a cloud-based computing architecture can be challenging. Fog cloud computing is… More >

  • Open Access

    ARTICLE

    Blockchain-Based Message Authentication Scheme for Internet of Vehicles in an Edge Computing Environment

    Qiping Zou1, Zhong Ruan2,*, Huaning Song1

    Computer Systems Science and Engineering, Vol.48, No.5, pp. 1301-1328, 2024, DOI:10.32604/csse.2024.051796 - 13 September 2024

    Abstract As an important application of intelligent transportation system, Internet of Vehicles (IoV) provides great convenience for users. Users can obtain real-time traffic conditions through the IoV’s services, plan users' travel routes, and improve travel efficiency. However, in the IoV system, there are always malicious vehicle nodes publishing false information. Therefore, it is essential to ensure the legitimacy of the source. In addition, during the peak period of vehicle travel, the vehicle releases a large number of messages, and IoV authentication efficiency is prone to performance bottlenecks. Most existing authentication schemes have the problem of low… More >

  • Open Access

    ARTICLE

    Machine Learning Enabled Novel Real-Time IoT Targeted DoS/DDoS Cyber Attack Detection System

    Abdullah Alabdulatif1, Navod Neranjan Thilakarathne2,*, Mohamed Aashiq3,*

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3655-3683, 2024, DOI:10.32604/cmc.2024.054610 - 12 September 2024

    Abstract The increasing prevalence of Internet of Things (IoT) devices has introduced a new phase of connectivity in recent years and, concurrently, has opened the floodgates for growing cyber threats. Among the myriad of potential attacks, Denial of Service (DoS) attacks and Distributed Denial of Service (DDoS) attacks remain a dominant concern due to their capability to render services inoperable by overwhelming systems with an influx of traffic. As IoT devices often lack the inherent security measures found in more mature computing platforms, the need for robust DoS/DDoS detection systems tailored to IoT is paramount for… More >

  • Open Access

    ARTICLE

    Improving Prediction Efficiency of Machine Learning Models for Cardiovascular Disease in IoST-Based Systems through Hyperparameter Optimization

    Tajim Md. Niamat Ullah Akhund1,2,*, Waleed M. Al-Nuwaiser3

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3485-3506, 2024, DOI:10.32604/cmc.2024.054222 - 12 September 2024

    Abstract This study explores the impact of hyperparameter optimization on machine learning models for predicting cardiovascular disease using data from an IoST (Internet of Sensing Things) device. Ten distinct machine learning approaches were implemented and systematically evaluated before and after hyperparameter tuning. Significant improvements were observed across various models, with SVM and Neural Networks consistently showing enhanced performance metrics such as F1-Score, recall, and precision. The study underscores the critical role of tailored hyperparameter tuning in optimizing these models, revealing diverse outcomes among algorithms. Decision Trees and Random Forests exhibited stable performance throughout the evaluation. While More >

  • Open Access

    ARTICLE

    Enhanced Mechanism for Link Failure Rerouting in Software-Defined Exchange Point Networks

    Abdijalil Abdullahi1,2, Selvakumar Manickam2,*

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4361-4385, 2024, DOI:10.32604/cmc.2024.054215 - 12 September 2024

    Abstract Internet Exchange Point (IXP) is a system that increases network bandwidth performance. Internet exchange points facilitate interconnection among network providers, including Internet Service Providers (ISPs) and Content Delivery Providers (CDNs). To improve service management, Internet exchange point providers have adopted the Software Defined Network (SDN) paradigm. This implementation is known as a Software-Defined Exchange Point (SDX). It improves network providers’ operations and management. However, performance issues still exist, particularly with multi-hop topologies. These issues include switch memory costs, packet processing latency, and link failure recovery delays. The paper proposes Enhanced Link Failure Rerouting (ELFR), an… More >

  • Open Access

    ARTICLE

    Blockchain-Based Certificateless Cross-Domain Authentication Scheme in the Industrial Internet of Things

    Zhaobin Li*, Xiantao Liu*, Nan Zhang, Zhanzhen Wei

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3835-3854, 2024, DOI:10.32604/cmc.2024.053950 - 12 September 2024

    Abstract The Industrial Internet of Things (IIoT) consists of massive devices in different management domains, and the lack of trust among cross-domain entities leads to risks of data security and privacy leakage during information exchange. To address the above challenges, a viable solution that combines Certificateless Public Key Cryptography (CL-PKC) with blockchain technology can be utilized. However, as many existing schemes rely on a single Key Generation Center (KGC), they are prone to problems such as single points of failure and high computational overhead. In this case, this paper proposes a novel blockchain-based certificateless cross-domain authentication… More >

  • Open Access

    ARTICLE

    Internet of Things Enabled DDoS Attack Detection Using Pigeon Inspired Optimization Algorithm with Deep Learning Approach

    Turki Ali Alghamdi, Saud S. Alotaibi*

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4047-4064, 2024, DOI:10.32604/cmc.2024.052796 - 12 September 2024

    Abstract Internet of Things (IoTs) provides better solutions in various fields, namely healthcare, smart transportation, home, etc. Recognizing Denial of Service (DoS) outbreaks in IoT platforms is significant in certifying the accessibility and integrity of IoT systems. Deep learning (DL) models outperform in detecting complex, non-linear relationships, allowing them to effectually severe slight deviations from normal IoT activities that may designate a DoS outbreak. The uninterrupted observation and real-time detection actions of DL participate in accurate and rapid detection, permitting proactive reduction events to be executed, hence securing the IoT network’s safety and functionality. Subsequently, this… More >

  • Open Access

    ARTICLE

    Human Intelligent-Things Interaction Application Using 6G and Deep Edge Learning

    Ftoon H. Kedwan*, Mohammed Abdur Rahman

    Journal on Internet of Things, Vol.6, pp. 43-73, 2024, DOI:10.32604/jiot.2024.052325 - 10 September 2024

    Abstract Impressive advancements and novel techniques have been witnessed in AI-based Human Intelligent-Things Interaction (HITI) systems. Several technological breakthroughs have contributed to HITI, such as Internet of Things (IoT), deep and edge learning for deducing intelligence, and 6G for ultra-fast and ultralow-latency communication between cyber-physical HITI systems. However, human-AI teaming presents several challenges that are yet to be addressed, despite the many advancements that have been made towards human-AI teaming. Allowing human stakeholders to understand AI’s decision-making process is a novel challenge. Artificial Intelligence (AI) needs to adopt diversified human understandable features, such as ethics, non-biases,… More >

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