Hongrong Wang^1,2, Xinjian Li^3,4, Xingjing She¹, Wenjian Ma^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2039-2055, 2025, DOI:10.32604/cmes.2025.072572 - 26 November 2025

Abstract In modern complex systems, real-time regression prediction plays a vital role in performance evaluation and risk warning. Nevertheless, existing methods still face challenges in maintaining stability and predictive accuracy under complex conditions. To address these limitations, this study proposes an online prediction approach that integrates event tracking sensitivity analysis with machine learning. Specifically, a real-time event tracking sensitivity analysis method is employed to capture and quantify the impact of key events on system outputs. On this basis, a mutual-information–based self-extraction mechanism is introduced to construct prior weights, which are then incorporated into a LightGBM prediction More >

Seungyeon Baek^1,#, Jueun Jeon^2,#, Byeonghui Jeong¹, Young-Sik Jeong^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 1213-1236, 2025, DOI:10.32604/cmes.2025.070679 - 30 October 2025

Abstract With the advent of the sixth-generation wireless technology, the importance of using artificial intelligence of things (AIoT) devices is increasing to enhance efficiency. As massive volumes of data are collected and stored in these AIoT environments, each device becomes a potential attack target, leading to increased security vulnerabilities. Therefore, intrusion detection studies have been conducted to detect malicious network traffic. However, existing studies have been biased toward conducting in-depth analyses of individual packets to improve accuracy or applying flow-based statistical information to ensure real-time performance. Effectively responding to complex and multifaceted threats in large-scale AIoT… More >

Huayu Li^1,2, Xinxin Chen^1,2, Lizhuang Tan^3,4,*, Konstantin I. Kostromitin^5,6, Athanasios V. Vasilakos⁷, Peiying Zhang^1,2

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 4133-4153, 2025, DOI:10.32604/cmc.2025.069690 - 23 September 2025

Abstract To address the challenge of missing modal information in entity alignment and to mitigate information loss or bias arising from modal heterogeneity during fusion, while also capturing shared information across modalities, this paper proposes a Multi-modal Pre-synergistic Entity Alignment model based on Cross-modal Mutual Information Strategy Optimization (MPSEA). The model first employs independent encoders to process multi-modal features, including text, images, and numerical values. Next, a multi-modal pre-synergistic fusion mechanism integrates graph structural and visual modal features into the textual modality as preparatory information. This pre-fusion strategy enables unified perception of heterogeneous modalities at the More >

Tong Wu^*, Dawei Zhou

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3067-3094, 2025, DOI:10.32604/cmc.2025.066525 - 23 September 2025

Abstract Fault attacks have emerged as an increasingly effective approach for integrated circuit security attacks due to their short execution time and minimal data requirement. However, the lack of a unified leakage model remains a critical challenge, as existing methods often rely on algorithm-specific details or prior knowledge of plaintexts and intermediate values. This paper proposes the Fault Probability Model based on Hamming Weight (FPHW) to address this. This novel statistical framework quantifies fault attacks by solely analyzing the statistical response of the target device, eliminating the need for attack algorithm details or implementation specifics. Building… More >

Adam Khalid¹, Anazida Zainal¹, Fuad A. Ghaleb², Bander Ali Saleh Al-rimy³, Yussuf Ahmed^2,*

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5665-5691, 2025, DOI:10.32604/cmc.2025.063451 - 30 July 2025

Abstract Advanced Persistent Threats (APTs) represent one of the most complex and dangerous categories of cyber-attacks characterised by their stealthy behaviour, long-term persistence, and ability to bypass traditional detection systems. The complexity of real-world network data poses significant challenges in detection. Machine learning models have shown promise in detecting APTs; however, their performance often suffers when trained on large datasets with redundant or irrelevant features. This study presents a novel, hybrid feature selection method designed to improve APT detection by reducing dimensionality while preserving the informative characteristics of the data. It combines Mutual Information (MI), Symmetric… More >

Manzheng Yuan^1,2, Kai Yang^2,*

Journal of Cyber Security, Vol.7, pp. 197-219, 2025, DOI:10.32604/jcs.2025.066089 - 07 July 2025

Abstract Currently, network intrusion detection systems (NIDS) face significant challenges in feature redundancy and high computational complexity, which hinder the improvement of detection performance and significantly reduce operational efficiency. To address these issues, this paper proposes an innovative weighted feature selection method combining mutual information and Extreme Gradient Boosting (XGBoost). This method aims to leverage their strengths to identify crucial feature subsets for intrusion detection accurately. Specifically, it first calculates the mutual information scores between features and target variables to evaluate individual discriminatory capabilities of features and uses XGBoost to obtain feature importance scores reflecting their… More >

Yifan Yu, Dazhi Wang^*, Yanhua Chen, Hongfeng Wang, Min Huang

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 3761-3780, 2024, DOI:10.32604/cmc.2024.057103 - 19 December 2024

Abstract For data mining tasks on large-scale data, feature selection is a pivotal stage that plays an important role in removing redundant or irrelevant features while improving classifier performance. Traditional wrapper feature selection methodologies typically require extensive model training and evaluation, which cannot deliver desired outcomes within a reasonable computing time. In this paper, an innovative wrapper approach termed Contribution Tracking Feature Selection (CTFS) is proposed for feature selection of large-scale data, which can locate informative features without population-level evolution. In other words, fewer evaluations are needed for CTFS compared to other evolutionary methods. We initially More >

Abbas Ali Hassan, Fardin Abdali-Mohammadi^*

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 971-983, 2024, DOI:10.32604/cmc.2024.053817 - 15 October 2024

Abstract From a medical perspective, the 12 leads of the heart in an electrocardiogram (ECG) signal have functional dependencies with each other. Therefore, all these leads report different aspects of an arrhythmia. Their differences lie in the level of highlighting and displaying information about that arrhythmia. For example, although all leads show traces of atrial excitation, this function is more evident in lead II than in any other lead. In this article, a new model was proposed using ECG functional and structural dependencies between heart leads. In the prescreening stage, the ECG signals are segmented from… More >

Sheng Xu¹, Shixiong Xiang², Feiyu Meng¹, Qiang Wu^1,*

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4197-4218, 2024, DOI:10.32604/cmc.2024.055157 - 12 September 2024

Abstract In Unsupervised Domain Adaptation (UDA) for person re-identification (re-ID), the primary challenge is reducing the distribution discrepancy between the source and target domains. This can be achieved by implicitly or explicitly constructing an appropriate intermediate domain to enhance recognition capability on the target domain. Implicit construction is difficult due to the absence of intermediate state supervision, making smooth knowledge transfer from the source to the target domain a challenge. To explicitly construct the most suitable intermediate domain for the model to gradually adapt to the feature distribution changes from the source to the target domain,… More >

Xu Sun, Yinhui Yu^*, Qing Cheng

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4149-4171, 2024, DOI:10.32604/cmc.2024.052725 - 12 September 2024

Abstract Object detection in unmanned aerial vehicle (UAV) aerial images has become increasingly important in military and civil applications. General object detection models are not robust enough against interclass similarity and intraclass variability of small objects, and UAV-specific nuisances such as uncontrolled weather conditions. Unlike previous approaches focusing on high-level semantic information, we report the importance of underlying features to improve detection accuracy and robustness from the information-theoretic perspective. Specifically, we propose a robust and discriminative feature learning approach through mutual information maximization (RD-MIM), which can be integrated into numerous object detection methods for aerial images.… More >