Fatemeh Ahmadi Abkenari^*, Melika Zandi, Shanmugapriya Gopalakrishnan

Journal of Cyber Security, Vol.8, pp. 93-109, 2026, DOI:10.32604/jcs.2026.074197 - 20 January 2026

Abstract Nowadays, cyberattacks are considered a significant threat not only to the reputation of organizations through the theft of customers’ data or reducing operational throughput, but also to their data ownership and the safety and security of their operations. In recent decades, machine learning techniques have been widely employed in cybersecurity research to detect various types of cyberattacks. In the domain of cybersecurity data, and especially in Trojan detection datasets, it is common for datasets to record multiple statistical measures for a single concept. We referred to them as SWMF features in this paper, which include… More >

Mohammed Alnusayri¹, Ghulam Mujtaba², Nouf Abdullah Almujally³, Shuoa S. Aitarbi⁴, Asaad Algarni⁵, Ahmad Jalal^2,6, Jeongmin Park^7,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071804 - 12 January 2026

Abstract This paper presents a unified Unmanned Aerial Vehicle-based (UAV-based) traffic monitoring framework that integrates vehicle detection, tracking, counting, motion prediction, and classification in a modular and co-optimized pipeline. Unlike prior works that address these tasks in isolation, our approach combines You Only Look Once (YOLO) v10 detection, ByteTrack tracking, optical-flow density estimation, Long Short-Term Memory-based (LSTM-based) trajectory forecasting, and hybrid Speeded-Up Robust Feature (SURF) + Gray-Level Co-occurrence Matrix (GLCM) feature engineering with VGG16 classification. Upon the validation across datasets (UAVDT and UAVID) our framework achieved a detection accuracy of 94.2%, and 92.3% detection accuracy when More >

Yahya Alhaj Maz¹, Mohammed Anbar¹, Selvakumar Manickam^1,*, Mosleh M. Abualhaj², Sultan Ahmed Almalki³, Basim Ahmad Alabsi⁴

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5287-5307, 2025, DOI:10.32604/cmc.2025.068257 - 23 October 2025

Abstract The Internet of Things (IoT) is an innovation that combines imagined space with the actual world on a single platform. Because of the recent rapid rise of IoT devices, there has been a lack of standards, leading to a massive increase in unprotected devices connecting to networks. Consequently, cyberattacks on IoT are becoming more common, particularly keylogging attacks, which are often caused by security vulnerabilities on IoT networks. This research focuses on the role of transfer learning and ensemble classifiers in enhancing the detection of keylogging attacks within small, imbalanced IoT datasets. The authors propose… More >

Lakshmi Alekhya Jandhyam^1,*, Ragupathy Rengaswamy¹, Narayana Satyala²

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3679-3714, 2025, DOI:10.32604/cmes.2025.068941 - 30 September 2025

Abstract Human Activity Recognition (HAR) has become increasingly critical in civic surveillance, medical care monitoring, and institutional protection. Current deep learning-based approaches often suffer from excessive computational complexity, limited generalizability under varying conditions, and compromised real-time performance. To counter these, this paper introduces an Active Learning-aided Heuristic Deep Spatio-Textural Ensemble Learning (ALH-DSEL) framework. The model initially identifies keyframes from the surveillance videos with a Multi-Constraint Active Learning (MCAL) approach, with features extracted from DenseNet121. The frames are then segmented employing an optimized Fuzzy C-Means clustering algorithm with Firefly to identify areas of interest. A deep ensemble More >

Rongwei Yu¹, Jie Yin^1,*, Jingyi Xiang¹, Qiyun Shao², Lina Wang¹

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 365-391, 2025, DOI:10.32604/cmc.2025.064438 - 09 June 2025

Abstract With the emergence of new attack techniques, traffic classifiers usually fail to maintain the expected performance in real-world network environments. In order to have sufficient generalizability to deal with unknown malicious samples, they require a large number of new samples for retraining. Considering the cost of data collection and labeling, data augmentation is an ideal solution. We propose an optimized noise-based traffic data augmentation system, ONTDAS. The system uses a gradient-based searching algorithm and an improved Bayesian optimizer to obtain optimized noise. The noise is injected into the original samples for data augmentation. Then, an More >

Louyi Jiang^1,#, Sulei Wang^1,#, Jiang Xie¹, Haiya Wang², Wei Shao^3,*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5445-5460, 2025, DOI:10.32604/cmc.2025.061759 - 19 May 2025

Abstract Carotid artery plaques represent a major contributor to the morbidity and mortality associated with cerebrovascular disease, and their clinical significance is largely determined by the risk linked to plaque vulnerability. Therefore, classifying plaque risk constitutes one of the most critical tasks in the clinical management of this condition. While classification models derived from individual medical centers have been extensively investigated, these single-center models often fail to generalize well to multi-center data due to variations in ultrasound images caused by differences in physician expertise and equipment. To address this limitation, a Dual-Classifier Label Correction Network model… More >

Yawar Abbas¹, Aisha Ahmed Alarfaj², Ebtisam Abdullah Alabdulqader³, Asaad Algarni⁴, Ahmad Jalal^1,5, Hui Liu^6,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4759-4776, 2025, DOI:10.32604/cmc.2025.059224 - 06 March 2025

Abstract Human Activity Recognition (HAR) in drone-captured videos has become popular because of the interest in various fields such as video surveillance, sports analysis, and human-robot interaction. However, recognizing actions from such videos poses the following challenges: variations of human motion, the complexity of backdrops, motion blurs, occlusions, and restricted camera angles. This research presents a human activity recognition system to address these challenges by working with drones’ red-green-blue (RGB) videos. The first step in the proposed system involves partitioning videos into frames and then using bilateral filtering to improve the quality of object foregrounds while… More >

Ahlem Walha¹, Amel Ksibi^2,*, Mohammed Zakariah^3,*, Manel Ayadi², Tagrid Alshalali², Oumaima Saidani², Leila Jamel², Nouf Abdullah Almujally²

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2959-3001, 2025, DOI:10.32604/cmes.2025.060545 - 03 March 2025

Abstract Dementia is a neurological disorder that affects the brain and its functioning, and women experience its effects more than men do. Preventive care often requires non-invasive and rapid tests, yet conventional diagnostic techniques are time-consuming and invasive. One of the most effective ways to diagnose dementia is by analyzing a patient’s speech, which is cheap and does not require surgery. This research aims to determine the effectiveness of deep learning (DL) and machine learning (ML) structures in diagnosing dementia based on women’s speech patterns. The study analyzes data drawn from the Pitt Corpus, which contains… More >

Xing Wang^1,*, Huazhen Liu¹, Abdelazim G. Hussien², Gang Hu¹, Li Zhang³

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2791-2839, 2025, DOI:10.32604/cmes.2025.058473 - 03 March 2025

Abstract Feature selection (FS) is essential in machine learning (ML) and data mapping by its ability to preprocess high-dimensional data. By selecting a subset of relevant features, feature selection cuts down on the dimension of the data. It excludes irrelevant or surplus features, thus boosting the performance and efficiency of the model. Particle Swarm Optimization (PSO) boasts a streamlined algorithmic framework and exhibits rapid convergence traits. Compared with other algorithms, it incurs reduced computational expenses when tackling high-dimensional datasets. However, PSO faces challenges like inadequate convergence precision. Therefore, regarding FS problems, this paper presents a binary… More >

Suneel Kumar Rath¹, Madhusmita Sahu¹, Shom Prasad Das², Junali Jasmine Jena³, Chitralekha Jena⁴, Baseem Khan^5,6,7,*, Ahmed Ali⁷, Pitshou Bokoro⁷

Computer Systems Science and Engineering, Vol.48, No.6, pp. 1513-1536, 2024, DOI:10.32604/csse.2024.057067 - 22 November 2024

Abstract Redundancy, correlation, feature irrelevance, and missing samples are just a few problems that make it difficult to analyze software defect data. Additionally, it might be challenging to maintain an even distribution of data relating to both defective and non-defective software. The latter software class’s data are predominately present in the dataset in the majority of experimental situations. The objective of this review study is to demonstrate the effectiveness of combining ensemble learning and feature selection in improving the performance of defect classification. Besides the successful feature selection approach, a novel variant of the ensemble learning… More >