Ali Hassan¹, N. Nizam-Uddin², Asim Quddus³, Syed Rizwan Hassan⁴, Ateeq Ur Rehman^5,*, Salil Bharany⁶

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 3499-3559, 2024, DOI:10.32604/cmc.2024.057877 - 19 December 2024

Abstract Enhancing the interconnection of devices and systems, the Internet of Things (IoT) is a paradigm-shifting technology. IoT security concerns are still a substantial concern despite its extraordinary advantages. This paper offers an extensive review of IoT security, emphasizing the technology’s architecture, important security elements, and common attacks. It highlights how important artificial intelligence (AI) is to bolstering IoT security, especially when it comes to addressing risks at different IoT architecture layers. We systematically examined current mitigation strategies and their effectiveness, highlighting contemporary challenges with practical solutions and case studies from a range of industries, such More >

Maryam Mahsal Khan¹, Attaullah Buriro², Tahir Ahmad^3,*, Subhan Ullah⁴

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4691-4705, 2024, DOI:10.32604/cmc.2024.057648 - 19 December 2024

Abstract With the ever-increasing continuous adoption of Industrial Internet of Things (IoT) technologies, security concerns have grown exponentially, especially regarding securing critical infrastructures. This is primarily due to the potential for backdoors to provide unauthorized access, disrupt operations, and compromise sensitive data. Backdoors pose a significant threat to the integrity and security of Industrial IoT setups by exploiting vulnerabilities and bypassing standard authentication processes. Hence its detection becomes of paramount importance. This paper not only investigates the capabilities of Machine Learning (ML) models in identifying backdoor malware but also evaluates the impact of balancing the dataset More >

Surbhi Bhatia Khan^1,2,*, Mohammad Alojail³, Mahesh Thyluru Ramakrishna⁴, Hemant Sharma⁵

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4895-4914, 2024, DOI:10.32604/cmc.2024.057585 - 19 December 2024

Abstract In Intelligent Manufacturing, Big Data and industrial information enable enterprises to closely monitor and respond to precise changes in both internal processes and external environmental factors, ensuring more informed decision-making and adaptive system management. It also promotes decision making and provides scientific analysis to enhance the efficiency of the operation, cost reduction, maximizing the process of production and so on. Various methods are employed to enhance productivity, yet achieving sustainable manufacturing remains a complex challenge that requires careful consideration. This study aims to develop a methodology for effective manufacturing sustainability by proposing a novel Hybrid… More >

Brij B. Gupta^1,2,3,4,*, Akshat Gaurav⁵, Varsha Arya^6,7, Razaz Waheeb Attar⁸, Shavi Bansal⁹, Ahmed Alhomoud¹⁰, Kwok Tai Chui¹¹

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4109-4124, 2024, DOI:10.32604/cmc.2024.056476 - 19 December 2024

Abstract Phishing attacks seriously threaten information privacy and security within the Internet of Things (IoT) ecosystem. Numerous phishing attack detection solutions have been developed for IoT; however, many of these are either not optimally efficient or lack the lightweight characteristics needed for practical application. This paper proposes and optimizes a lightweight deep-learning model for phishing attack detection. Our model employs a two-fold optimization approach: first, it utilizes the analysis of the variance (ANOVA) F-test to select the optimal features for phishing detection, and second, it applies the Cuckoo Search algorithm to tune the hyperparameters (learning rate… More >

Ivan Couée^*

Phyton-International Journal of Experimental Botany, Vol.93, No.11, pp. 2707-2714, 2024, DOI:10.32604/phyton.2024.058101 - 30 November 2024

Abstract The 15-carbon terpenoid abscisic acid (ABA) acts in vascular plants as a versatile hormone playing essential roles in reproductive development, vegetative development and growth, stress-development interactions, and physiological responses to abiotic and biotic stresses. Over the past 60 years, ABA dynamics, regulation, and responses have been progressively characterized: synthesis, transport and translocation, conjugation and deconjugation, metabolism, sensing, signal transduction, and downstream responses. In this context, the discovery of ABA exporters and importers has added novel dimensions to the understanding of ABA regulation. Moreover, since the initial discovery of the adenosine triphosphate-binding cassette (ABC) AtABCG25 exporter… More >

Farida Asriani^1,2, Azhari Azhari^1,*, Wahyono Wahyono¹

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3079-3096, 2024, DOI:10.32604/cmc.2024.058193 - 18 November 2024

Abstract Incredible progress has been made in human action recognition (HAR), significantly impacting computer vision applications in sports analytics. However, identifying dynamic and complex movements in sports like badminton remains challenging due to the need for precise recognition accuracy and better management of complex motion patterns. Deep learning techniques like convolutional neural networks (CNNs), long short-term memory (LSTM), and graph convolutional networks (GCNs) improve recognition in large datasets, while the traditional machine learning methods like SVM (support vector machines), RF (random forest), and LR (logistic regression), combined with handcrafted features and ensemble approaches, perform well but… More >

Samsul Huda^1,*, Yasuyuki Nogami², Maya Rahayu², Takuma Akada², Md. Biplob Hossain², Muhammad Bisri Musthafa², Yang Jie², Le Hoang Anh²

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3165-3187, 2024, DOI:10.32604/cmc.2024.058144 - 18 November 2024

Abstract Global food security is a pressing issue that affects the stability and well-being of communities worldwide. While existing Internet of Things (IoT) enabled plant monitoring systems have made significant strides in agricultural monitoring, they often face limitations such as high power consumption, restricted mobility, complex deployment requirements, and inadequate security measures for data access. This paper introduces an enhanced IoT application for agricultural monitoring systems that address these critical shortcomings. Our system strategically combines power efficiency, portability, and secure access capabilities, assisting farmers in monitoring and tracking crop environmental conditions. The proposed system includes a… More >

Deema Alsekait¹, Mohammed Zakariah², Syed Umar Amin^3,*, Zafar Iqbal Khan³, Jehad Saad Alqurni⁴

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2395-2436, 2024, DOI:10.32604/cmc.2024.055469 - 18 November 2024

Abstract The widespread adoption of Internet of Things (IoT) devices has resulted in notable progress in different fields, improving operational effectiveness while also raising concerns about privacy due to their vulnerability to virus attacks. Further, the study suggests using an advanced approach that utilizes machine learning, specifically the Wide Residual Network (WRN), to identify hidden malware in IoT systems. The research intends to improve privacy protection by accurately identifying malicious software that undermines the security of IoT devices, using the MalMemAnalysis dataset. Moreover, thorough experimentation provides evidence for the effectiveness of the WRN-based strategy, resulting in… More >

Xiaojun Jin¹, Chao Ma^2,*, Song Luo², Pengyi Zeng¹, Yifei Wei¹

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2317-2336, 2024, DOI:10.32604/cmc.2024.055344 - 18 November 2024

Abstract Federated learning enables data owners in the Internet of Things (IoT) to collaborate in training models without sharing private data, creating new business opportunities for building a data market. However, in practical operation, there are still some problems with federated learning applications. Blockchain has the characteristics of decentralization, distribution, and security. The blockchain-enabled federated learning further improve the security and performance of model training, while also expanding the application scope of federated learning. Blockchain has natural financial attributes that help establish a federated learning data market. However, the data of federated learning tasks may be… More >

Bassam Mohammad Elzaghmouri¹, Yosef Hasan Fayez Jbara², Said Elaiwat³, Nisreen Innab^4,*, Ahmed Abdelgader Fadol Osman⁵, Mohammed Awad Mohammed Ataelfadiel⁵, Farah H. Zawaideh⁶, Mouiad Fadeil Alawneh⁷, Asef Al-Khateeb⁸, Marwan Abu-Zanona⁸

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2299-2316, 2024, DOI:10.32604/cmc.2024.054836 - 18 November 2024

Abstract As the Internet of Things (IoT) continues to expand, incorporating a vast array of devices into a digital ecosystem also increases the risk of cyber threats, necessitating robust defense mechanisms. This paper presents an innovative hybrid deep learning architecture that excels at detecting IoT threats in real-world settings. Our proposed model combines Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory (BLSTM), Gated Recurrent Units (GRU), and Attention mechanisms into a cohesive framework. This integrated structure aims to enhance the detection and classification of complex cyber threats while accommodating the operational constraints of diverse IoT systems.… More >