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 >

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 >

Abdullah Alabdulatif¹, Navod Neranjan Thilakarathne^2,*, Mohamed Aashiq^3,*

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 >

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 >

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 >

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 >

Haibo Wang¹, Hongwei Gao^1,*, Pai Jiang¹, Matthieu De Mari², Panzer Gu³, Yinsheng Liu¹

Journal on Internet of Things, Vol.6, pp. 17-41, 2024, DOI:10.32604/jiot.2024.054791 - 26 August 2024

Abstract In the 6G Internet of Things (IoT) paradigm, unprecedented challenges will be raised to provide massive connectivity, ultra-low latency, and energy efficiency for ultra-dense IoT devices. To address these challenges, we explore the non-orthogonal multiple access (NOMA) based grant-free random access (GFRA) schemes in the cellular uplink to support massive IoT devices with high spectrum efficiency and low access latency. In particular, we focus on optimizing the backoff strategy of each device when transmitting time-sensitive data samples to a multiple-input multiple-output (MIMO)-enabled base station subject to energy constraints. To cope with the dynamic varied channel… More >

Arpita Dinesh Sarang¹, Mohsen Ali Alawami², Ki-Woong Park^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 655-669, 2024, DOI:10.32604/cmes.2024.053434 - 20 August 2024

Abstract Nowadays, the use of Avatars that are unique digital depictions has increased by users to access Metaverse—a virtual reality environment—through multiple devices and for various purposes. Therefore, the Avatar and Metaverse are being developed with a new theory, application, and design, necessitating the association of more personal data and devices of targeted users every day. This Avatar and Metaverse technology explosion raises privacy and security concerns, leading to cyber attacks. MV-Honeypot, or Metaverse-Honeypot, as a commercial off-the-shelf solution that can counter these cyber attack-causing vulnerabilities, should be developed. To fill this gap, we study user’s More > Graphic Abstract

Peicong He, Yang Xin^*, Yixian Yang

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3067-3084, 2024, DOI:10.32604/cmc.2024.054777 - 15 August 2024

Abstract The proliferation of intelligent, connected Internet of Things (IoT) devices facilitates data collection. However, task workers may be reluctant to participate in data collection due to privacy concerns, and task requesters may be concerned about the validity of the collected data. Hence, it is vital to evaluate the quality of the data collected by the task workers while protecting privacy in spatial crowdsourcing (SC) data collection tasks with IoT. To this end, this paper proposes a privacy-preserving data reliability evaluation for SC in IoT, named PARE. First, we design a data uploading format using blockchain More >

Mengmeng Zhao^1,2,3, Haipeng Peng^1,2,*, Lixiang Li^1,2, Yeqing Ren^1,2

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2815-2837, 2024, DOI:10.32604/cmc.2024.053765 - 15 August 2024

Abstract In the Industrial Internet of Things (IIoT), sensors generate time series data to reflect the working state. When the systems are attacked, timely identification of outliers in time series is critical to ensure security. Although many anomaly detection methods have been proposed, the temporal correlation of the time series over the same sensor and the state (spatial) correlation between different sensors are rarely considered simultaneously in these methods. Owing to the superior capability of Transformer in learning time series features. This paper proposes a time series anomaly detection method based on a spatial-temporal network and… More >