Charlotte Olivia Namagembe, Mohamad Ibrahim, Md Arafatur Rahman^*, Prashant Pillai

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-20, 2026, DOI:10.32604/cmc.2025.070316 - 09 December 2025

Abstract The rapid proliferation of commercial unmanned aerial vehicles (UAVs) has revolutionized fields such as precision agriculture and disaster response. However, their heavy reliance on GPS navigation leaves them highly vulnerable to spoofing attacks, with potentially severe consequences. To mitigate this threat, we present a machine learning-driven framework for real-time GPS spoofing detection, designed with a balance of detection accuracy and computational efficiency. Our work is distinguished by the creation of a comprehensive dataset of 10,000 instances that integrates both simulated and real-world data, enabling robust and generalizable model development. A comprehensive evaluation of multiple classification More >

Sen Yang¹, Jie Zhong¹, Boyu Gan¹, Yi Sun¹, Changming Bu¹, Mingtao Zhang¹, Jiehong Li^1,*, Yang Yu^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 2943-2967, 2025, DOI:10.32604/cmes.2025.067282 - 30 September 2025

Abstract Foam concrete is widely used in engineering due to its lightweight and high porosity. Its compressive strength, a key performance indicator, is influenced by multiple factors, showing nonlinear variation. As compressive strength tests for foam concrete take a long time, a fast and accurate prediction method is needed. In recent years, machine learning has become a powerful tool for predicting the compressive strength of cement-based materials. However, existing studies often use a limited number of input parameters, and the prediction accuracy of machine learning models under the influence of multiple parameters and nonlinearity remains unclear.… More >

Asad Iqbal¹, Mehdi Hussain^1,*, Qaiser Riaz¹, Madiha Khalid¹, Rafia Mumtaz¹, Ki-Hyun Jung^2,*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1693-1714, 2025, DOI:10.32604/cmc.2025.064260 - 29 August 2025

Abstract Ransomware, particularly crypto-ransomware, remains a significant cybersecurity challenge, encrypting victim data and demanding a ransom, often leaving the data irretrievable even if payment is made. This study proposes an early detection approach to mitigate such threats by identifying ransomware activity before the encryption process begins. The approach employs a two-tiered approach: a signature-based method using hashing techniques to match known threats and a dynamic behavior-based analysis leveraging Cuckoo Sandbox and machine learning algorithms. A critical feature is the integration of the most effective Application Programming Interface call monitoring, which analyzes system-level interactions such as file More >

Petr Opěla^1,*, Josef Walek^1,*, Jaromír Kopeček²

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 713-732, 2025, DOI:10.32604/cmes.2024.055219 - 17 December 2024

Abstract In engineering practice, it is often necessary to determine functional relationships between dependent and independent variables. These relationships can be highly nonlinear, and classical regression approaches cannot always provide sufficiently reliable solutions. Nevertheless, Machine Learning (ML) techniques, which offer advanced regression tools to address complicated engineering issues, have been developed and widely explored. This study investigates the selected ML techniques to evaluate their suitability for application in the hot deformation behavior of metallic materials. The ML-based regression methods of Artificial Neural Networks (ANNs), Support Vector Machine (SVM), Decision Tree Regression (DTR), and Gaussian Process Regression More >

Fizza Mansoor¹, Muhammad Affan Alim^2,5,*, Muhammad Taha Jilani³, Muhammad Monsoor Alam^4,5, Mazliham Mohd Su’ud⁵

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4603-4624, 2024, DOI:10.32604/cmc.2024.057979 - 19 December 2024

Abstract Software cost estimation is a crucial aspect of software project management, significantly impacting productivity and planning. This research investigates the impact of various feature selection techniques on software cost estimation accuracy using the CoCoMo NASA dataset, which comprises data from 93 unique software projects with 24 attributes. By applying multiple machine learning algorithms alongside three feature selection methods, this study aims to reduce data redundancy and enhance model accuracy. Our findings reveal that the principal component analysis (PCA)-based feature selection technique achieved the highest performance, underscoring the importance of optimal feature selection in improving software More >

Mujahid Ali^*

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2161-2194, 2024, DOI:10.32604/cmc.2024.058888 - 18 November 2024

Abstract Forecasting travel demand requires a grasp of individual decision-making behavior. However, transport mode choice (TMC) is determined by personal and contextual factors that vary from person to person. Numerous characteristics have a substantial impact on travel behavior (TB), which makes it important to take into account while studying transport options. Traditional statistical techniques frequently presume linear correlations, but real-world data rarely follows these presumptions, which may make it harder to grasp the complex interactions. Thorough systematic review was conducted to examine how machine learning (ML) approaches might successfully capture nonlinear correlations that conventional methods may… More >

Noor Ullah Bacha¹, Songfeng Lu¹, Attiq Ur Rehman¹, Muhammad Idrees², Yazeed Yasin Ghadi³, Tahani Jaser Alahmadi^4,*

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 707-748, 2024, DOI:10.32604/cmc.2024.054780 - 15 October 2024

Abstract Cross-Site Scripting (XSS) remains a significant threat to web application security, exploiting vulnerabilities to hijack user sessions and steal sensitive data. Traditional detection methods often fail to keep pace with the evolving sophistication of cyber threats. This paper introduces a novel hybrid ensemble learning framework that leverages a combination of advanced machine learning algorithms—Logistic Regression (LR), Support Vector Machines (SVM), eXtreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Deep Neural Networks (DNN). Utilizing the XSS-Attacks-2021 dataset, which comprises 460 instances across various real-world traffic-related scenarios, this framework significantly enhances XSS attack detection. Our approach, which… More >

Ahsan Wajahat¹, Jingsha He¹, Nafei Zhu¹, Tariq Mahmood^2,3, Tanzila Saba², Amjad Rehman Khan², Faten S. Alamri^4,*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 651-673, 2024, DOI:10.32604/cmc.2024.047530 - 25 April 2024

Abstract The growing usage of Android smartphones has led to a significant rise in incidents of Android malware and privacy breaches. This escalating security concern necessitates the development of advanced technologies capable of automatically detecting and mitigating malicious activities in Android applications (apps). Such technologies are crucial for safeguarding user data and maintaining the integrity of mobile devices in an increasingly digital world. Current methods employed to detect sensitive data leaks in Android apps are hampered by two major limitations they require substantial computational resources and are prone to a high frequency of false positives. This… More >

Nasser Alshammari¹, Shumaila Shahzadi², Saad Awadh Alanazi^1,*, Shahid Naseem³, Muhammad Anwar³, Madallah Alruwaili⁴, Muhammad Rizwan Abid⁵, Omar Alruwaili⁴, Ahmed Alsayat¹, Fahad Ahmad^6,7

Computer Systems Science and Engineering, Vol.48, No.2, pp. 363-394, 2024, DOI:10.32604/csse.2023.040721 - 19 March 2024

Abstract Software Defined Network (SDN) and Network Function Virtualization (NFV) technology promote several benefits to network operators, including reduced maintenance costs, increased network operational performance, simplified network lifecycle, and policies management. Network vulnerabilities try to modify services provided by Network Function Virtualization MANagement and Orchestration (NFV MANO), and malicious attacks in different scenarios disrupt the NFV Orchestrator (NFVO) and Virtualized Infrastructure Manager (VIM) lifecycle management related to network services or individual Virtualized Network Function (VNF). This paper proposes an anomaly detection mechanism that monitors threats in NFV MANO and manages promptly and adaptively to implement and… More >

Vaishnawi Priyadarshni¹, Sanjay Kumar Sharma¹, Mohammad Khalid Imam Rahmani^2,*, Baijnath Kaushik³, Rania Almajalid^2,*

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2441-2468, 2024, DOI:10.32604/cmc.2024.044963 - 27 February 2024

Abstract Breast cancer (BC) is one of the leading causes of death among women worldwide, as it has emerged as the most commonly diagnosed malignancy in women. Early detection and effective treatment of BC can help save women’s lives. Developing an efficient technology-based detection system can lead to non-destructive and preliminary cancer detection techniques. This paper proposes a comprehensive framework that can effectively diagnose cancerous cells from benign cells using the Curated Breast Imaging Subset of the Digital Database for Screening Mammography (CBIS-DDSM) data set. The novelty of the proposed framework lies in the integration of More >