Jizhao Liu, Minghao Guo^*

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 817-842, 2025, DOI:10.32604/cmc.2024.057660 - 03 January 2025

Abstract The increasing popularity of the Internet and the widespread use of information technology have led to a rise in the number and sophistication of network attacks and security threats. Intrusion detection systems are crucial to network security, playing a pivotal role in safeguarding networks from potential threats. However, in the context of an evolving landscape of sophisticated and elusive attacks, existing intrusion detection methodologies often overlook critical aspects such as changes in network topology over time and interactions between hosts. To address these issues, this paper proposes a real-time network intrusion detection method based on… More >

Xueyan Ding^1,2, Xiyu Chen¹, Jiaxin Wang¹, Jianxin Zhang^1,2,*

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 713-727, 2025, DOI:10.32604/cmc.2024.057655 - 03 January 2025

Abstract Underwater target detection is extensively applied in domains such as underwater search and rescue, environmental monitoring, and marine resource surveys. It is crucial in enabling autonomous underwater robot operations and promoting ocean exploration. Nevertheless, low imaging quality, harsh underwater environments, and obscured objects considerably increase the difficulty of detecting underwater targets, making it difficult for current detection methods to achieve optimal performance. In order to enhance underwater object perception and improve target detection precision, we propose a lightweight underwater target detection method using You Only Look Once (YOLO) v8 with multi-scale cross-channel attention (MSCCA), named… More >

Yuewei Wu¹, Ruiling Fu¹, Tongtong Xing¹, Fulian Yin^1,2,*

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 751-775, 2025, DOI:10.32604/cmc.2024.057606 - 03 January 2025

Abstract In the Internet era, recommendation systems play a crucial role in helping users find relevant information from large datasets. Class imbalance is known to severely affect data quality, and therefore reduce the performance of recommendation systems. Due to the imbalance, machine learning algorithms tend to classify inputs into the positive (majority) class every time to achieve high prediction accuracy. Imbalance can be categorized such as by features and classes, but most studies consider only class imbalance. In this paper, we propose a recommendation system that can integrate multiple networks to adapt to a large number… More >

Zhe Li, Yun Liang, Jinyu Wang, Yang Gao^*

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 1171-1192, 2025, DOI:10.32604/cmc.2024.057225 - 03 January 2025

Abstract Iced transmission line galloping poses a significant threat to the safety and reliability of power systems, leading directly to line tripping, disconnections, and power outages. Existing early warning methods of iced transmission line galloping suffer from issues such as reliance on a single data source, neglect of irregular time series, and lack of attention-based closed-loop feedback, resulting in high rates of missed and false alarms. To address these challenges, we propose an Internet of Things (IoT) empowered early warning method of transmission line galloping that integrates time series data from optical fiber sensing and weather… More >

Peng Li, Yanrui Wei, Lili Yin^*

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 609-625, 2025, DOI:10.32604/cmc.2024.056651 - 03 January 2025

Abstract Stock price prediction is a typical complex time series prediction problem characterized by dynamics, nonlinearity, and complexity. This paper introduces a generative adversarial network model that incorporates an attention mechanism (GAN-LSTM-Attention) to improve the accuracy of stock price prediction. Firstly, the generator of this model combines the Long and Short-Term Memory Network (LSTM), the Attention Mechanism and, the Fully-Connected Layer, focusing on generating the predicted stock price. The discriminator combines the Convolutional Neural Network (CNN) and the Fully-Connected Layer to discriminate between real stock prices and generated stock prices. Secondly, to evaluate the practical application… More >

Xirui Yang, Chen Zhang^*

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 3781-3804, 2024, DOI:10.32604/cmc.2024.057131 - 19 December 2024

Abstract User location trajectory refers to the sequence of geographic location information that records the user’s movement or stay within a period of time and is usually used in mobile crowd sensing networks, in which the user participates in the sensing task, the process of sensing data collection faces the problem of privacy leakage. To address the privacy leakage issue of trajectory data during uploading, publishing, and sharing when users use location services on mobile smart group sensing terminal devices, this paper proposes a privacy protection method based on generative adversarial networks and attention mechanisms (BiLS-A-GAN).… More >

Mingyuan Zhai^1,2, Hanquan Zhang¹, Le Wang¹, Dong Xiao^1,*, Zhengmin Gu³, Zhenni Li¹

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3241-3260, 2024, DOI:10.32604/cmc.2024.056241 - 18 November 2024

Abstract In the process of dense vehicles traveling fast, there will be mutual occlusion between vehicles, which will lead to the problem of deterioration of the tracking effect of different vehicles, so this paper proposes a research method of virtual simulation video vehicle target tracking based on you only look once (YOLO)v5s and deep simple online and realtime tracking (DeepSort). Given that the DeepSort algorithm is currently the most effective tracking method, this paper merges the YOLOv5 algorithm with the DeepSort algorithm. Then it adds the efficient channel attention networks (ECA-Net) focusing mechanism at the back… More >

Bassam Mohammad Elzaghmouri¹, Yosef Hasan Fayez Jbara², Said Elaiwat³, Nisreen Innab^4,*, Ahmed Abdelgader Fadol Osman⁵, Mohammed Awad Mohammed Ataelfadiel⁵, Farah H. Zawaideh⁶, Mouiad Fadeil Alawneh⁷, Asef Al-Khateeb⁸, Marwan Abu-Zanona⁸

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2299-2316, 2024, DOI:10.32604/cmc.2024.054836 - 18 November 2024

Abstract As the Internet of Things (IoT) continues to expand, incorporating a vast array of devices into a digital ecosystem also increases the risk of cyber threats, necessitating robust defense mechanisms. This paper presents an innovative hybrid deep learning architecture that excels at detecting IoT threats in real-world settings. Our proposed model combines Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory (BLSTM), Gated Recurrent Units (GRU), and Attention mechanisms into a cohesive framework. This integrated structure aims to enhance the detection and classification of complex cyber threats while accommodating the operational constraints of diverse IoT systems.… More >

Jiaqi Gao¹, Kangfeng Zheng^1,*, Xiujuan Wang², Chunhua Wu¹, Bin Wu²

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 251-270, 2024, DOI:10.32604/cmc.2024.055560 - 15 October 2024

Abstract User identity linkage (UIL) refers to identifying user accounts belonging to the same identity across different social media platforms. Most of the current research is based on text analysis, which fails to fully explore the rich image resources generated by users, and the existing attempts touch on the multimodal domain, but still face the challenge of semantic differences between text and images. Given this, we investigate the UIL task across different social media platforms based on multimodal user-generated contents (UGCs). We innovatively introduce the efficient user identity linkage via aligned multi-modal features and temporal correlation… More >

Yunfei Ban, Damin Zhang^*, Qing He, Qianwen Shen

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 567-601, 2024, DOI:10.32604/cmc.2024.055007 - 15 October 2024

Abstract The surge in connected devices and massive data aggregation has expanded the scale of the Internet of Things (IoT) networks. The proliferation of unknown attacks and related risks, such as zero-day attacks and Distributed Denial of Service (DDoS) attacks triggered by botnets, have resulted in information leakage and property damage. Therefore, developing an efficient and realistic intrusion detection system (IDS) is critical for ensuring IoT network security. In recent years, traditional machine learning techniques have struggled to learn the complex associations between multidimensional features in network traffic, and the excellent performance of deep learning techniques,… More >