Mahmoud E. Farfoura^1,*, Ahmad Alkhatib¹, Deema Mohammed Alsekait^2,*, Mohammad Alshinwan^3,7, Sahar A. El-Rahman⁴, Didi Rosiyadi⁵, Diaa Salama AbdElminaam^6,7

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4833-4857, 2024, DOI:10.32604/cmc.2024.054849

Abstract The article describes a new method for malware classification, based on a Machine Learning (ML) model architecture specifically designed for malware detection, enabling real-time and accurate malware identification. Using an innovative feature dimensionality reduction technique called the Interpolation-based Feature Dimensionality Reduction Technique (IFDRT), the authors have significantly reduced the feature space while retaining critical information necessary for malware classification. This technique optimizes the model’s performance and reduces computational requirements. The proposed method is demonstrated by applying it to the BODMAS malware dataset, which contains 57,293 malware samples and 77,142 benign samples, each with a 2381-feature… More >

Kwang-Cheol Rim¹, Young-Min Yoon², Sung-Uk Kim³, Jeong-In Kim^4,*

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3903-3916, 2024, DOI:10.32604/cmc.2024.054620

Abstract The identification and mitigation of anomaly data, characterized by deviations from normal patterns or singularities, stand as critical endeavors in modern technological landscapes, spanning domains such as Non-Fungible Tokens (NFTs), cyber-security, and the burgeoning metaverse. This paper presents a novel proposal aimed at refining anomaly detection methodologies, with a particular focus on continuous data streams. The essence of the proposed approach lies in analyzing the rate of change within such data streams, leveraging this dynamic aspect to discern anomalies with heightened precision and efficacy. Through empirical evaluation, our method demonstrates a marked improvement over existing More >

Abdullah Alabdulatif¹, Navod Neranjan Thilakarathne^2,*, Mohamed Aashiq^3,*

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3655-3683, 2024, DOI:10.32604/cmc.2024.054610

Abstract The increasing prevalence of Internet of Things (IoT) devices has introduced a new phase of connectivity in recent years and, concurrently, has opened the floodgates for growing cyber threats. Among the myriad of potential attacks, Denial of Service (DoS) attacks and Distributed Denial of Service (DDoS) attacks remain a dominant concern due to their capability to render services inoperable by overwhelming systems with an influx of traffic. As IoT devices often lack the inherent security measures found in more mature computing platforms, the need for robust DoS/DDoS detection systems tailored to IoT is paramount for… More >

Fan Li^1,2, Shuyao Zhang³, Jie Yang^1,2,*, Zhicheng Feng^1,2, Zhichao Chen^1,2

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3819-3833, 2024, DOI:10.32604/cmc.2024.054525

Abstract Aiming at the limitations of the existing railway foreign object detection methods based on two-dimensional (2D) images, such as short detection distance, strong influence of environment and lack of distance information, we propose Rail-PillarNet, a three-dimensional (3D) LIDAR (Light Detection and Ranging) railway foreign object detection method based on the improvement of PointPillars. Firstly, the parallel attention pillar encoder (PAPE) is designed to fully extract the features of the pillars and alleviate the problem of local fine-grained information loss in PointPillars pillars encoder. Secondly, a fine backbone network is designed to improve the feature extraction… More >

Hawazen Alzahrani¹, Tarek Sheltami¹, Abdulaziz Barnawi², Muhammad Imam^2,*, Ansar Yaser³

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4703-4728, 2024, DOI:10.32604/cmc.2024.054203

Abstract The Internet of Things (IoT) links various devices to digital services and significantly improves the quality of our lives. However, as IoT connectivity is growing rapidly, so do the risks of network vulnerabilities and threats. Many interesting Intrusion Detection Systems (IDSs) are presented based on machine learning (ML) techniques to overcome this problem. Given the resource limitations of fog computing environments, a lightweight IDS is essential. This paper introduces a hybrid deep learning (DL) method that combines convolutional neural networks (CNN) and long short-term memory (LSTM) to build an energy-aware, anomaly-based IDS. We test this… More >

Zhongmin Huangfu, Shuqing Li^*, Luoheng Yan

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3713-3731, 2024, DOI:10.32604/cmc.2024.054188

Abstract Addressing the challenges in detecting surface floating litter in artificial lakes, including complex environments, uneven illumination, and susceptibility to noise and weather, this paper proposes an efficient and lightweight Ghost-YOLO (You Only Look Once) v8 algorithm. The algorithm integrates advanced attention mechanisms and a small-target detection head to significantly enhance detection performance and efficiency. Firstly, an SE (Squeeze-and-Excitation) mechanism is incorporated into the backbone network to fortify the extraction of resilient features and precise target localization. This mechanism models feature channel dependencies, enabling adaptive adjustment of channel importance, thereby improving recognition of floating litter targets.… More >

Xiaoyu Liu, Yong Hu^*

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4413-4432, 2024, DOI:10.32604/cmc.2024.053938

Abstract Multi-label image classification is recognized as an important task within the field of computer vision, a discipline that has experienced a significant escalation in research endeavors in recent years. The widespread adoption of convolutional neural networks (CNNs) has catalyzed the remarkable success of architectures such as ResNet-101 within the domain of image classification. However, in multi-label image classification tasks, it is crucial to consider the correlation between labels. In order to improve the accuracy and performance of multi-label classification and fully combine visual and semantic features, many existing studies use graph convolutional networks (GCN) for… More >

Huan Wang¹, Hong Wang¹, Zhongyuan Jiang^2,*, Qing Qian¹, Yong Long¹

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4603-4620, 2024, DOI:10.32604/cmc.2024.053740

Abstract Copy-Move Forgery Detection (CMFD) is a technique that is designed to identify image tampering and locate suspicious areas. However, the practicality of the CMFD is impeded by the scarcity of datasets, inadequate quality and quantity, and a narrow range of applicable tasks. These limitations significantly restrict the capacity and applicability of CMFD. To overcome the limitations of existing methods, a novel solution called IMTNet is proposed for CMFD by employing a feature decoupling approach. Firstly, this study formulates the objective task and network relationship as an optimization problem using transfer learning. Furthermore, it thoroughly discusses… More >

Changyu Liu¹, Hao Huang¹, Guogang Huang^2,*, Chunyin Wu¹, Yingqi Liang³

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4219-4242, 2024, DOI:10.32604/cmc.2024.053625

Abstract Laboratory safety is a critical area of broad societal concern, particularly in the detection of abnormal actions. To enhance the efficiency and accuracy of detecting such actions, this paper introduces a novel method called TubeRAPT (Tubelet Transformer based on Adapter and Prefix Training Module). This method primarily comprises three key components: the TubeR network, an adaptive clustering attention mechanism, and a prefix training module. These components work in synergy to address the challenge of knowledge preservation in models pre-trained on large datasets while maintaining training efficiency. The TubeR network serves as the backbone for spatio-temporal… More >

Jing Wang^1,2,*, Tianwen Lin¹, Chen Zhang¹, Jun Peng^1,*

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4621-4641, 2024, DOI:10.32604/cmc.2024.053206

Abstract In this paper, a feature interactive bi-temporal change detection network (FIBTNet) is designed to solve the problem of pseudo change in remote sensing image building change detection. The network improves the accuracy of change detection through bi-temporal feature interaction. FIBTNet designs a bi-temporal feature exchange architecture (EXA) and a bi-temporal difference extraction architecture (DFA). EXA improves the feature exchange ability of the model encoding process through multiple space, channel or hybrid feature exchange methods, while DFA uses the change residual (CR) module to improve the ability of the model decoding process to extract different features More >