Fatma S. Alrayes¹, Syed Umar Amin^2,*, Nada Ali Hakami², Mohammed K. Alzaylaee³, Tariq Kashmeery⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 581-614, 2025, DOI:10.32604/cmes.2025.065188 - 31 July 2025

Abstract The growing incidence of cyberattacks necessitates a robust and effective Intrusion Detection Systems (IDS) for enhanced network security. While conventional IDSs can be unsuitable for detecting different and emerging attacks, there is a demand for better techniques to improve detection reliability. This study introduces a new method, the Deep Adaptive Multi-Layer Attention Network (DAMLAN), to boost the result of intrusion detection on network data. Due to its multi-scale attention mechanisms and graph features, DAMLAN aims to address both known and unknown intrusions. The real-world NSL-KDD dataset, a popular choice among IDS researchers, is used to… More >

Anjie Wang^1,2, Haining Jiao^1,2,*, Zhichao Chen^1,2,*, Jie Yang^1,2

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5773-5790, 2025, DOI:10.32604/cmc.2025.066852 - 30 July 2025

Abstract With the rapid development of the Internet of Things (IoT), artificial intelligence, and big data, waste-sorting systems must balance high accuracy, low latency, and resource efficiency. This paper presents an edge-friendly intelligent waste-sorting system that integrates a lightweight visual neural network, a pentagonal-trajectory robotic arm, and IoT connectivity to meet the requirements of real-time response and high accuracy. A lightweight object detection model, YOLO-WasNet (You Only Look Once for Waste Sorting Network), is proposed to optimize performance on edge devices. YOLO-WasNet adopts a lightweight backbone, applies Spatial Pyramid Pooling-Fast (SPPF) and Convolutional Block Attention Module… More >

Haiyan Chen^*, Rong Li

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5693-5712, 2025, DOI:10.32604/cmc.2025.065339 - 30 July 2025

Abstract The detection of Ochotona Curzoniae serves as a fundamental component for estimating the population size of this species and for analyzing the dynamics of its population fluctuations. In natural environments, the pixels representing Ochotona Curzoniae constitute a small fraction of the total pixels, and their distinguishing features are often subtle, complicating the target detection process. To effectively extract the characteristics of these small targets, a feature fusion approach that utilizes up-sampling and channel integration from various layers within a CNN can significantly enhance the representation of target features, ultimately improving detection accuracy. However, the top-down… More >

Xiaogang Yuan^*, Huan Pei, Yanlin Wu

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5555-5575, 2025, DOI:10.32604/cmc.2025.063537 - 30 July 2025

Abstract In the complex environment of Wireless Sensor Networks (WSNs), various malicious attacks have emerged, among which internal attacks pose particularly severe security risks. These attacks seriously threaten network stability, data transmission reliability, and overall performance. To effectively address this issue and significantly improve intrusion detection speed, accuracy, and resistance to malicious attacks, this research designs a Three-level Intrusion Detection Model based on Dynamic Trust Evaluation (TIDM-DTE). This study conducts a detailed analysis of how different attack types impact node trust and establishes node models for data trust, communication trust, and energy consumption trust by focusing… More >

Zhe Chen^*, Yinyang Zhang, Sihao Xing

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1787-1803, 2025, DOI:10.32604/cmc.2025.065238 - 09 June 2025

Abstract Unmanned aerial vehicle (UAV) imagery poses significant challenges for object detection due to extreme scale variations, high-density small targets (68% in VisDrone dataset), and complex backgrounds. While YOLO-series models achieve speed-accuracy trade-offs via fixed convolution kernels and manual feature fusion, their rigid architectures struggle with multi-scale adaptability, as exemplified by YOLOv8n’s 36.4% mAP and 13.9% small-object AP on VisDrone2019. This paper presents YOLO-LE, a lightweight framework addressing these limitations through three novel designs: (1) We introduce the C2f-Dy and LDown modules to enhance the backbone’s sensitivity to small-object features while reducing backbone parameters, thereby improving More >

Hui Xu, Jiahui Chen^*, Zhonghao Hu

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4495-4513, 2025, DOI:10.32604/cmc.2025.062189 - 19 May 2025

Abstract Nowadays, abnormal traffic detection for Software-Defined Networking (SDN) faces the challenges of large data volume and high dimensionality. Since traditional machine learning-based detection methods have the problem of data redundancy, the Metaheuristic Algorithm (MA) is introduced to select features before machine learning to reduce the dimensionality of data. Since a Tyrannosaurus Optimization Algorithm (TROA) has the advantages of few parameters, simple implementation, and fast convergence, and it shows better results in feature selection, TROA can be applied to abnormal traffic detection for SDN. However, TROA suffers from insufficient global search capability, is easily trapped in… More >

Alaa Mahmood, İsa Avcı^*

Computer Systems Science and Engineering, Vol.49, pp. 401-417, 2025, DOI:10.32604/csse.2025.062413 - 25 April 2025

Abstract A Distributed Denial-of-Service (DDoS) attack poses a significant challenge in the digital age, disrupting online services with operational and financial consequences. Detecting such attacks requires innovative and effective solutions. The primary challenge lies in selecting the best among several DDoS detection models. This study presents a framework that combines several DDoS detection models and Multiple-Criteria Decision-Making (MCDM) techniques to compare and select the most effective models. The framework integrates a decision matrix from training several models on the CiC-DDOS2019 dataset with Fuzzy Weighted Zero Inconsistency Criterion (FWZIC) and Multi-Attribute Boundary Approximation Area Comparison (MABAC) methodologies.… More >

Pengcheng Gao^*, Zhenjiang Li

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4555-4572, 2025, DOI:10.32604/cmc.2025.060873 - 06 March 2025

Abstract The application of deep learning for target detection in aerial images captured by Unmanned Aerial Vehicles (UAV) has emerged as a prominent research focus. Due to the considerable distance between UAVs and the photographed objects, coupled with complex shooting environments, existing models often struggle to achieve accurate real-time target detection. In this paper, a You Only Look Once v8 (YOLOv8) model is modified from four aspects: the detection head, the up-sampling module, the feature extraction module, and the parameter optimization of positive sample screening, and the YOLO-S3DT model is proposed to improve the performance of More >

Yue Yang^1,2, Xiangyan Tang^2,3,*, Zhaowu Liu^2,3,*, Jieren Cheng^2,3, Haozhe Fang³, Cunyi Zhang³

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4389-4408, 2025, DOI:10.32604/cmc.2025.060357 - 06 March 2025

Abstract With the rapid development of Internet of Things technology, the sharp increase in network devices and their inherent security vulnerabilities present a stark contrast, bringing unprecedented challenges to the field of network security, especially in identifying malicious attacks. However, due to the uneven distribution of network traffic data, particularly the imbalance between attack traffic and normal traffic, as well as the imbalance between minority class attacks and majority class attacks, traditional machine learning detection algorithms have significant limitations when dealing with sparse network traffic data. To effectively tackle this challenge, we have designed a lightweight… More >

Yicong Yu^1,2, Kaixin Lin¹, Jiajun Hong¹, Rong-Guei Tsai^3,*, Yuanzhi Huang¹

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 913-928, 2025, DOI:10.32604/cmc.2024.058467 - 03 January 2025

Abstract In recent years, the number of patients with colon disease has increased significantly. Colon polyps are the precursor lesions of colon cancer. If not diagnosed in time, they can easily develop into colon cancer, posing a serious threat to patients’ lives and health. A colonoscopy is an important means of detecting colon polyps. However, in polyp imaging, due to the large differences and diverse types of polyps in size, shape, color, etc., traditional detection methods face the problem of high false positive rates, which creates problems for doctors during the diagnosis process. In order to… More >