Tong Zu, Fengyong Li^*

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1395-1417, 2024, DOI:10.32604/cmes.2024.055442 - 27 September 2024

Abstract False data injection attack (FDIA) can affect the state estimation of the power grid by tampering with the measured value of the power grid data, and then destroying the stable operation of the smart grid. Existing work usually trains a detection model by fusing the data-driven features from diverse power data streams. Data-driven features, however, cannot effectively capture the differences between noisy data and attack samples. As a result, slight noise disturbances in the power grid may cause a large number of false detections for FDIA attacks. To address this problem, this paper designs a… More >

Xinchang Zhang¹, Xiaokang Pan², Rongjie Zhu³, Runda Guan², Zhongfeng Qiu⁴, Biao Song^5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1827-1846, 2024, DOI:10.32604/cmes.2024.054298 - 27 September 2024

Abstract Identification of ocean eddies from a large amount of ocean data provided by satellite measurements and numerical simulations is crucial, while the academia has invented many traditional physical methods with accurate detection capability, but their detection computational efficiency is low. In recent years, with the increasing application of deep learning in ocean feature detection, many deep learning-based eddy detection models have been developed for more effective eddy detection from ocean data. But it is difficult for them to precisely fit some physical features implicit in traditional methods, leading to inaccurate identification of ocean eddies. In… More >

Daasara Keshavamurthy Archana¹, Doddabhadrappla Gowda Prakasha¹, Pundikala Veeresha², Kottakkaran Sooppy Nisar^3,4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1347-1363, 2024, DOI:10.32604/cmes.2024.053916 - 27 September 2024

Abstract This study intends to examine the analytical solutions to the resulting one-dimensional differential equation of a cancer tumor model in the frame of time-fractional order with the Caputo-fractional operator employing a highly efficient methodology called the -homotopy analysis transform method. So, the preferred approach effectively found the analytic series solution of the proposed model. The procured outcomes of the present framework demonstrated that this method is authentic for obtaining solutions to a time-fractional-order cancer model. The results achieved graphically specify that the concerned paradigm is dependent on arbitrary order and parameters and also disclose the More >

Khawaja Tahir Mehmood^1,2,*, Shahid Atiq¹, Intisar Ali Sajjad³, Muhammad Majid Hussain⁴, Malik M. Abdul Basit²

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1673-1708, 2024, DOI:10.32604/cmes.2024.053903 - 27 September 2024

Abstract Software-Defined Networking (SDN), with segregated data and control planes, provides faster data routing, stability, and enhanced quality metrics, such as throughput (T_h), maximum available bandwidth (B_d(max)), data transfer (D_Transfer), and reduction in end-to-end delay (D_(E-E)). This paper explores the critical work of deploying SDN in large­scale Data Center Networks (DCNs) to enhance its Quality of Service (QoS) parameters, using logically distributed control configurations. There is a noticeable increase in Delay_(E-E) when adopting SDN with a unified (single) control structure in big DCNs to handle Hypertext Transfer Protocol (HTTP) requests causing a reduction in network quality parameters (B_d(max), T_h, D_Transfer, D_(E-E),… More > Graphic Abstract

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 >

Il Hwan Ji¹, Ju Hyeon Lee¹, Seungho Jeon², Jung Taek Seo^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1519-1549, 2024, DOI:10.32604/cmes.2024.053437 - 27 September 2024

Abstract In the early days of IoT’s introduction, it was challenging to introduce encryption communication due to the lack of performance of each component, such as computing resources like CPUs and batteries, to encrypt and decrypt data. Because IoT is applied and utilized in many important fields, a cyberattack on IoT can result in astronomical financial and human casualties. For this reason, the application of encrypted communication to IoT has been required, and the application of encrypted communication to IoT has become possible due to improvements in the computing performance of IoT devices and the development… More >

Oluwaseun Peter Ige^1,2, Keng Hoon Gan^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1847-1865, 2024, DOI:10.32604/cmes.2024.053373 - 27 September 2024

Abstract Feature selection is a crucial technique in text classification for improving the efficiency and effectiveness of classifiers or machine learning techniques by reducing the dataset’s dimensionality. This involves eliminating irrelevant, redundant, and noisy features to streamline the classification process. Various methods, from single feature selection techniques to ensemble filter-wrapper methods, have been used in the literature. Metaheuristic algorithms have become popular due to their ability to handle optimization complexity and the continuous influx of text documents. Feature selection is inherently multi-objective, balancing the enhancement of feature relevance, accuracy, and the reduction of redundant features. This… More >

Rajan Aravind^1,2, Sundararajan Natarajan¹, Krishnankutty Jayakumar², Ratna Kumar Annabattula^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 997-1032, 2024, DOI:10.32604/cmes.2024.053047 - 27 September 2024

Abstract Understanding the probabilistic nature of brittle materials due to inherent dispersions in their mechanical properties is important to assess their reliability and safety for sensitive engineering applications. This is all the more important when elements composed of brittle materials are exposed to dynamic environments, resulting in catastrophic fatigue failures. The authors propose the application of a non-intrusive polynomial chaos expansion method for probabilistic studies on brittle materials undergoing fatigue fracture when geometrical parameters and material properties are random independent variables. Understanding the probabilistic nature of fatigue fracture in brittle materials is crucial for ensuring the… More >

Ye-Seul Kil^1,#, Yu-Ran Jeon^1,#, Sun-Jin Lee¹, Il-Gu Lee^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1473-1493, 2024, DOI:10.32604/cmes.2024.052637 - 27 September 2024

Abstract With the rise of remote work and the digital industry, advanced cyberattacks have become more diverse and complex in terms of attack types and characteristics, rendering them difficult to detect with conventional intrusion detection methods. Signature-based intrusion detection methods can be used to detect attacks; however, they cannot detect new malware. Endpoint detection and response (EDR) tools are attracting attention as a means of detecting attacks on endpoints in real-time to overcome the limitations of signature-based intrusion detection techniques. However, EDR tools are restricted by the continuous generation of unnecessary logs, resulting in poor detection… More >

Mohamed A. Mahdi¹, Suliman Mohamed Fati^2,*, Mohamed A.G. Hazber¹, Shahanawaj Ahamad³, Sawsan A. Saad⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1651-1671, 2024, DOI:10.32604/cmes.2024.052291 - 27 September 2024

Abstract Cyberbullying, a critical concern for digital safety, necessitates effective linguistic analysis tools that can navigate the complexities of language use in online spaces. To tackle this challenge, our study introduces a new approach employing Bidirectional Encoder Representations from the Transformers (BERT) base model (cased), originally pretrained in English. This model is uniquely adapted to recognize the intricate nuances of Arabic online communication, a key aspect often overlooked in conventional cyberbullying detection methods. Our model is an end-to-end solution that has been fine-tuned on a diverse dataset of Arabic social media (SM) tweets showing a notable… More >