Huang Zeng, Mingtian Zhang, Tengfei Liu, Anjia Yang^*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5125-5142, 2024, DOI:10.32604/cmc.2024.052846

Abstract Federated learning is an important distributed model training technique in Internet of Things (IoT), in which participant selection is a key component that plays a role in improving training efficiency and model accuracy. This module enables a central server to select a subset of participants to perform model training based on data and device information. By doing so, selected participants are rewarded and actively perform model training, while participants that are detrimental to training efficiency and model accuracy are excluded. However, in practice, participants may suspect that the central server may have miscalculated and thus… More >

Yehao Long, Jing Zhong^*, Tongdi Zhang, Li Chen, Lijun Zhang^*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3435-3453, 2024, DOI:10.32604/cmc.2024.051629

Abstract Physical Vapor Deposited (PVD) TiAlN coatings are extensively utilized as protective layers for cutting tools, renowned for their excellent comprehensive performance. To optimize quality control of TiAlN coatings for cutting tools, a multi-scale simulation approach is proposed that encompasses the microstructure evolution of coatings considering the entire preparation and service lifecycle of PVD TiAlN coatings. This scheme employs phase-field simulation to capture the essential microstructure of the PVD-prepared TiAlN coatings. Moreover, cutting simulation is used to determine the service temperature experienced during cutting processes at varying rates. Cahn-Hilliard modeling is finally utilized to consume the More >

Tingting Su¹, Jia Wang^1,*, Wei Hu^2,*, Gaoqiang Dong¹, Jeon Gwanggil³

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4433-4448, 2024, DOI:10.32604/cmc.2024.051535

Abstract Along with the progression of Internet of Things (IoT) technology, network terminals are becoming continuously more intelligent. IoT has been widely applied in various scenarios, including urban infrastructure, transportation, industry, personal life, and other socio-economic fields. The introduction of deep learning has brought new security challenges, like an increment in abnormal traffic, which threatens network security. Insufficient feature extraction leads to less accurate classification results. In abnormal traffic detection, the data of network traffic is high-dimensional and complex. This data not only increases the computational burden of model training but also makes information extraction more… More >

Jingjing Nie^1,*, Zhenhua Chen²

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4763-4779, 2024, DOI:10.32604/cmc.2024.051181

Abstract The Internet of Medical Things (IoMT) is an application of the Internet of Things (IoT) in the medical field. It is a cutting-edge technique that connects medical sensors and their applications to healthcare systems, which is essential in smart healthcare. However, Personal Health Records (PHRs) are normally kept in public cloud servers controlled by IoMT service providers, so privacy and security incidents may be frequent. Fortunately, Searchable Encryption (SE), which can be used to execute queries on encrypted data, can address the issue above. Nevertheless, most existing SE schemes cannot solve the vector dominance threshold… More >

Mohammad Aldossary^1,*, Hatem A. Alharbi², Nasir Ayub³

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4603-4620, 2024, DOI:10.32604/cmc.2024.050862

Abstract Cloud computing has become increasingly popular due to its capacity to perform computations without relying on physical infrastructure, thereby revolutionizing computer processes. However, the rising energy consumption in cloud centers poses a significant challenge, especially with the escalating energy costs. This paper tackles this issue by introducing efficient solutions for data placement and node management, with a clear emphasis on the crucial role of the Internet of Things (IoT) throughout the research process. The IoT assumes a pivotal role in this study by actively collecting real-time data from various sensors strategically positioned in and around… More >

Sen-Hui Wang^1,2,*, Xi Kang¹, Cheng Wang¹, Tian-Bing Ma¹, Xiang He², Ke Yang^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1405-1427, 2024, DOI:10.32604/cmes.2024.049281

Abstract Accurately predicting the remaining useful life (RUL) of bearings in mining rotating equipment is vital for mining enterprises. This research aims to distinguish the features associated with the RUL of bearings and propose a prediction model based on these selected features. This study proposes a hybrid predictive model to assess the RUL of rolling element bearings. The proposed model begins with the pre-processing of bearing vibration signals to reconstruct sixty time-domain features. The hybrid model selects relevant features from the sixty time-domain features of the vibration signal by adopting the RReliefF feature selection algorithm. Subsequently,… More >

So-Eun Jeon¹, Ye-Sol Oh¹, Yeon-Ji Lee¹, Il-Gu Lee^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1669-1687, 2024, DOI:10.32604/cmes.2024.047239

Abstract With the advancement of wireless network technology, vast amounts of traffic have been generated, and malicious traffic attacks that threaten the network environment are becoming increasingly sophisticated. While signature-based detection methods, static analysis, and dynamic analysis techniques have been previously explored for malicious traffic detection, they have limitations in identifying diversified malware traffic patterns. Recent research has been focused on the application of machine learning to detect these patterns. However, applying machine learning to lightweight devices like IoT devices is challenging because of the high computational demands and complexity involved in the learning process. In… More >

Yifan Liu¹, Shancang Li^1,*, Xinheng Wang², Li Xu³

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1233-1261, 2024, DOI:10.32604/cmes.2024.046473

Abstract The Industrial Internet of Things (IIoT) has brought numerous benefits, such as improved efficiency, smart analytics, and increased automation. However, it also exposes connected devices, users, applications, and data generated to cyber security threats that need to be addressed. This work investigates hybrid cyber threats (HCTs), which are now working on an entirely new level with the increasingly adopted IIoT. This work focuses on emerging methods to model, detect, and defend against hybrid cyber attacks using machine learning (ML) techniques. Specifically, a novel ML-based HCT modelling and analysis framework was proposed, in which regularisation and Random Forest were More >

Fatimah Mudhhi Alanazi^*, Bothina Abdelmeneem Elsobky, Shaimaa Aly Elmorsy

Computer Systems Science and Engineering, Vol.48, No.3, pp. 835-851, 2024, DOI:10.32604/csse.2024.042401

Abstract Proliferation of technology, coupled with networking growth, has catapulted cybersecurity to the forefront of modern security concerns. In this landscape, the precise detection of cyberattacks and anomalies within networks is crucial, necessitating the development of efficient intrusion detection systems (IDS). This article introduces a framework utilizing the fusion of fuzzy sets with support vector machines (SVM), named FSVM. The core strategy of FSVM lies in calculating the significance of network features to determine their relative importance. Features with minimal significance are prudently disregarded, a method akin to feature selection. This process not only curtails the… More >

Sohaib Latif^1,*, M. Saad Bin Ilyas¹, Azhar Imran², Hamad Ali Abosaq³, Abdulaziz Alzubaidi⁴, Vincent Karovič Jr.⁵

Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 353-379, 2024, DOI:10.32604/iasc.2024.047080

Abstract The Internet of Things (IoT) is growing rapidly and impacting almost every aspect of our lives, from wearables and healthcare to security, traffic management, and fleet management systems. This has generated massive volumes of data and security, and data privacy risks are increasing with the advancement of technology and network connections. Traditional access control solutions are inadequate for establishing access control in IoT systems to provide data protection owing to their vulnerability to single-point OF failure. Additionally, conventional privacy preservation methods have high latency costs and overhead for resource-constrained devices. Previous machine learning approaches were… More >