Bappa Muktar^*, Vincent Fono, Adama Nouboukpo

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4705-4727, 2025, DOI:10.32604/cmc.2025.067733 - 23 October 2025

Abstract Vehicular Ad Hoc Networks (VANETs) are central to Intelligent Transportation Systems (ITS), especially for real-time communication involving emergency vehicles. Yet, Distributed Denial of Service (DDoS) attacks can disrupt safety-critical channels and undermine reliability. This paper presents a robust, scalable framework for detecting DDoS attacks in highway VANETs. We construct a new dataset with Network Simulator 3 (NS-3) and Simulation of Urban Mobility (SUMO), enriched with real mobility traces from Germany’s A81 highway (OpenStreetMap). Three traffic classes are modeled: DDoS, Voice over IP (VoIP), and Transmission Control Protocol Based (TCP-based) video streaming (VideoTCP). The pipeline includes normalization,… More >

Syeda Zoupash Zahra¹, Najia Saher², Malik Muhammad Saad Missen³, Rab Nawaz Bashir^4,5, Salma Idris⁵, Tahani Jaser Alahmadi^6,*, Muhammad Inshal Khan⁵

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4869-4883, 2025, DOI:10.32604/cmc.2025.067639 - 23 October 2025

Abstract The increasing frequency and intensity of natural disasters necessitate advanced prediction techniques to mitigate potential damage. This study presents a comprehensive multi-hazard early warning framework by integrating the multi-source data fusion technique. A multi-source data extraction method was introduced by extracting pressure level and average precipitation data based on the hazard event from the Cooperative Open Online Landslide Repository (COOLR) dataset across multiple temporal intervals (12 h to 1 h prior to events). Feature engineering was performed using Choquet fuzzy integral-based importance scoring, which enables the model to account for interactions and uncertainty across multiple… More >

Yulin Yao, Kangfeng Zheng, Bin Wu^*, Chunhua Wu, Jiaqi Gao, Jvjie Wang, Minjiao Yang

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4753-4776, 2025, DOI:10.32604/cmc.2025.065059 - 23 October 2025

Abstract Cognitive biases are commonly used by attackers to manipulate users’ psychology in phishing emails. This study systematically analyzes the exploitation of cognitive biases in phishing emails and addresses the following questions: (1) Which cognitive biases are frequently exploited in phishing emails? (2) How are cognitive biases exploited in phishing emails? (3) How effective are cognitive bias features in detecting phishing emails? (4) How can the exploitation of cognitive biases in phishing emails be modelled? To address these questions, this study constructed a cognitive processing model that explains how attackers manipulate users by leveraging cognitive biases More >

Asif Nawaz^1,*, Rana Saud Shoukat², Mohammad Shehab¹, Khalil El Hindi³, Zohair Ahmed⁴

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4777-4793, 2025, DOI:10.32604/cmc.2025.064088 - 23 October 2025

Abstract The kingdom Animalia encompasses multicellular, eukaryotic organisms known as animals. Currently, there are approximately 1.5 million identified species of living animals, including over 195 distinct breeds of dogs. Each breed possesses unique characteristics that can be challenging to distinguish. Each breed has its own characteristics that are difficult to identify. Various computer-based methods, including machine learning, deep learning, transfer learning, and robotics, are employed to identify dog breeds, focusing mainly on image or voice data. Voice-based techniques often face challenges such as noise, distortion, and changes in frequency or pitch, which can impair the model’s… More >

Kadriye Simsek Alan^*, Ayca Durgut, Helin Doga Demirel

Journal on Artificial Intelligence, Vol.7, pp. 397-415, 2025, DOI:10.32604/jai.2025.071133 - 20 October 2025

Abstract Efficient utilization of processor and memory resources is essential for sustaining performance and energy efficiency in modern computing infrastructures. While earlier research has emphasized CPU utilization forecasting, joint prediction of CPU and memory usage under real workload conditions remains underexplored. This study introduces a machine learning–based framework for real-time prediction of CPU and RAM utilization using the Google Cluster Trace 2019 v3 dataset. The framework combines Extreme Gradient Boosting (XGBoost) with a MultiOutputRegressor (MOR) to capture nonlinear interactions across multiple resource dimensions, supported by a leakage-safe imputation strategy that prevents bias from missing values. Nested… More >

Xuezhi Wen^1,2, Eric Danso^1,2,*, Solomon Danso¹

Journal of Cyber Security, Vol.7, pp. 439-462, 2025, DOI:10.32604/jcs.2025.070587 - 17 October 2025

Abstract Cloud-based intrusion detection systems increasingly face sophisticated adversarial attacks such as evasion and poisoning that exploit vulnerabilities in traditional machine learning (ML) models. While deep learning (DL) offers superior detection accuracy for high-dimensional cloud logs, it remains vulnerable to adversarial perturbations and lacks interpretability. Conversely, Hidden Markov Models (HMMs) provide probabilistic reasoning but struggle with raw, sequential cloud data. To bridge this gap, we propose a Deep Learning-Enhanced Ensemble Hidden Markov Model (DL-HMM) framework that synergizes the strengths of Long Short-Term Memory (LSTM) networks and HMMs while incorporating adversarial training and ensemble learning. Our architecture… More >

Brian Mweu^1,*, John Ndia²

Journal of Cyber Security, Vol.7, pp. 417-437, 2025, DOI:10.32604/jcs.2025.071765 - 10 October 2025

Abstract Static analysis methods are crucial in developing secure software, as they allow for the early identification of vulnerabilities before the software is executed. This systematic review follows Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines to assess static analysis techniques for software security enhancement. We systematically searched IEEE Xplore, Association for Computing Machinery (ACM) Digital Library, SpringerLink, and ScienceDirect for journal articles published between 2017 and 2025. The review examines hybrid analyses and machine learning integration to enhance vulnerability detection accuracy. Static analysis tools enable early fault detection but face persistent challenges. More >

Jeyapaul Angel Ida Chellam¹, Bright Brailson Mansingh², Daniel Stalin Alex³, Joseph Selvi Binoj^4,*

Journal of Polymer Materials, Vol.42, No.3, pp. 773-794, 2025, DOI:10.32604/jpm.2025.069295 - 30 September 2025

Abstract Machine learning models can predict material properties quickly and accurately at a low computational cost. This study generated novel hybridized nanocomposites with unsaturated polyester resin as the matrix and Areca fruit husk fiber (AFHF), tamarind fruit fiber (TFF), and nano-sized coconut shell powder (NCSP). It is challenging to determine the optimal proportion of raw materials in this composite to achieve maximum mechanical properties. This task was accomplished with the help of ML techniques in this study. The tensile strength of the hybridized nanocomposite was increased by 134.06% compared to the neat unsaturated polyester resin at… More >

Victor Otieno Mony^*, Anselemo Peters Ikoha, Roselida O. Maroko

Journal of Cyber Security, Vol.7, pp. 395-415, 2025, DOI:10.32604/jcs.2025.070952 - 30 September 2025

Abstract The widespread adoption of Cloud-Based Services has significantly increased the surface area for cyber threats, particularly targeting authentication mechanisms, which remain among the most vulnerable components of cloud security. This study aimed to address these challenges by developing and evaluating an Intelligent Zero Trust Architecture model tailored to mitigate authentication-related threats in Cloud-Based Services environments. Data was sourced from public repositories, including Kaggle and the National Institute for Standards and Technology MITRE Corporation’s Adversarial Tactics, Techniques, & Common Knowledge (ATT&CK) framework. The study utilized two trust signals: Behavioral targeting system users and Contextual targeting system… More >

Raj Sonani¹, Reham Alhejaili^2,*, Pushpalika Chatterjee³, Khalid Hamad Alnafisah⁴, Jehad Ali^5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3169-3189, 2025, DOI:10.32604/cmes.2025.070225 - 30 September 2025

Abstract Healthcare networks are transitioning from manual records to electronic health records, but this shift introduces vulnerabilities such as secure communication issues, privacy concerns, and the presence of malicious nodes. Existing machine and deep learning-based anomalies detection methods often rely on centralized training, leading to reduced accuracy and potential privacy breaches. Therefore, this study proposes a Blockchain-based-Federated Learning architecture for Malicious Node Detection (BFL-MND) model. It trains models locally within healthcare clusters, sharing only model updates instead of patient data, preserving privacy and improving accuracy. Cloud and edge computing enhance the model’s scalability, while blockchain ensures More >