Peter Kipngeno Langat^*, Michael Kimwele, Dennis Kaburu

Journal of Cyber Security, Vol.8, pp. 541-558, 2026, DOI:10.32604/jcs.2026.083592 - 21 August 2026

Abstract Zero-day attacks pose a significant threat to computer systems and networks as they exploit weaknesses that have not been recognized by security professionals or software creators and for which there are no existing protective measures. This study introduced an innovative method for identifying Zero-day attacks through a Recurrent neural network model. To effectively mitigate these risks, not only is continuous monitoring essential, but also the implementation of machine learning. The model was trained on network traffic data and leveraged on the ability of Recurrent Neural Networks (RNNs) to learn complex patterns and identify anomalies that… More >

Zehan Li¹, Yingyi Li^2,*, Zhiwei Tang³, Xuemeng Zhai³, Jiandong Liang¹, Guangmin Hu³

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.086118 - 13 August 2026

Abstract Social network platforms have become primary channels for information dissemination, yet they are increasingly exploited by anomalous users such as bots, fake accounts, and coordinated disinformation spreaders. These malicious actors manipulate public opinion, spread misinformation and undermine platform integrity, posing severe threats to the security of the online ecosystem. Accurate detection of such users is challenging because they often organize into sophisticated high-order connection patterns that extend beyond local neighborhoods. Existing methods address this by either injecting predefined motifs as handcrafted features, which lack flexibility to discover unknown patterns, or employing higher-order Graph neural networks… More >

Xue-Wei Liu¹, Shao-Qiang Ye², Feng Qin³, Kai-Qing Zhou^1,*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.085316 - 13 August 2026

Abstract Weighted fuzzy production rules (WFPRs) provide superior expressiveness and interpretability in knowledge engineering area. However, manual construction of WFPRs is labor-intensive, time-consuming, and inherently subjective, which greatly restricts their practical application. The back propagation neural network (BPNN) has been widely adopted for automatic WFPR extraction. Nevertheless, its high sensitivity to initial weight configurations frequently results in premature convergence to local optima, generating redundant, poorly interpretable rule sets that compromise the inherent interpretability advantage of WFPRs. This paper proposes an elite dynamic scout-guided grey wolf optimizer (EDSG-GWO) and integrates it into a BPNN-based WFPR extraction framework… More >

Qi Li^1,2, Sathish Kumar Selvaperumal^1,*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.085073 - 13 August 2026

Abstract Unmanned Aerial Vehicle (UAV) air-to-ground (A2G) communication is a core enabling technology for emerging low-altitude wireless applications. At the same time, accurate real-time channel emulation remains a key bottleneck restricting its large-scale engineering deployment. Conventional universal channel simulators exhibit limited fidelity when modeling UAV-specific fading characteristics and degrade real-time performance on resource-constrained hardware platforms. In this study, we develop a dedicated UAV A2G channel simulator based on a heterogeneous FPGA platform (Processing System (PS) + Programmable Logic (PL)). To achieve high-precision path-loss prediction, we train a lightweight backpropagation neural network (BPNN) using field-measured data in… More >

David Hason Rudd^1,*, Cesar Sanin², Md Rafiqul Islam³, Xianzhi Wang¹, Huan Huo¹

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084015 - 13 August 2026

Abstract The accurate classification of human emotions from speech remains a formidable challenge due to the dynamic, non-stationary properties of audio signals and pervasive background noise. Traditional single-domain extraction methods frequently fail to capture overlapping acoustic phenomena, resulting in high misclassification rates among acoustically similar emotions. To overcome this, we propose the Dual-Decomposition Ensemble (DDE-SER), an architecture that synergizes 1D adaptive frequency filtering with 2D spatial spectrogram separation. The framework operates through two distinct pipelines: an adaptive time-domain branch that leverages VGG-optiVMD to autonomously extract Intrinsic Mode Functions (IMFs), and a structural spectrogram branch that applies… More >

Guan Yang¹, Haozhen Wang², Yu Wang^3,*, Weiguang Liu⁴, Bo Chen^5,6

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.083419 - 13 August 2026

Abstract The rapid proliferation of encrypted communication technologies, such as TLS, VPNs, and Tor, has significantly limited the effectiveness of traditional traffic classification methods that rely on port numbers or deep packet inspection. While handcrafted statistical features provide partial solutions, they often lack robustness and generalization in complex traffic scenarios. Although deep learning models such as CNNs and RNNs can capture local and sequential patterns, they typically overlook higher-order structural dependencies among bytes. To address these challenges, we propose DVG-GNN, a Dual-View Graph representation learning framework for encrypted traffic classification. The framework decomposes each packet into More >

Alanoud Al Mazroa¹, Abdulrahman Mohammed Alamoudi², Nurdaulet Karabayev³, Jawad Ahmad^4,*, Muhammad Shahbaz Khan⁵

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.083381 - 13 August 2026

Abstract The rapid expansion of the Internet of Things in critical industrial environments has significantly increased the attack surface, exposing systems to sophisticated cyber threats. Traditional pattern-based intrusion detection systems struggle to detect such advanced attacks, while deep learning approaches, despite achieving high detection accuracy, often suffer from high computational cost and latency, limiting their deployment in resource-constrained edge gateways. Quantum machine learning offers a promising alternative by enabling high-dimensional feature representation; however, current implementations are constrained by hardware noise, limited qubit availability and backend-dependent execution characteristics in the Noisy Intermediate-Scale Quantum era. To address these… More >

Aigerim Altayeva^1,*, Nurzhan Omarov²

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.080878 - 13 August 2026

Abstract Hazardous impulsive sound detection plays a critical role in intelligent surveillance, public safety monitoring, and automated emergency response systems. This study proposes a lightweight channel-attention-enhanced deep learning architecture for real-time detection and classification of hazardous acoustic events. The proposed framework utilizes mel-spectrogram representations to capture time-frequency characteristics of audio signals and employs a compact convolutional neural backbone to efficiently extract hierarchical features. To enhance feature discrimination, a squeeze-and-excitation channel-attention mechanism is integrated into the architecture, enabling adaptive recalibration of feature channels and improved robustness under noisy and complex acoustic environments. A custom dataset consisting of More >

Hafiz Khizer bin Talib¹, Yanlong Cao², Muhammad Zaman^3,*, Sharifah Sakinah Syed Ahmad⁴, Nikola Ivkovic⁵, Mario Konecki⁵, Adnan Akhunzada⁶

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.078779 - 13 August 2026

Abstract Micro-expression recognition (MER) is a demanding problem in affective computing because micro-expressions are brief, low-amplitude, involuntary facial movements that often reveal concealed affective states. Their recognition is complicated by weak muscle activation, short temporal duration, inter-subject variability, class imbalance, illumination changes, and the limited scale of publicly available MER datasets. To address these constraints, this paper introduces BroadAttNet, an attention-driven convolutional framework that embeds a Broadbent-inspired selective attention layer into a compact CNN backbone. The proposed layer learns to assign higher importance to discriminative facial regions while suppressing spatially redundant or noisy responses, thereby improving… More >

Jordán Pascual Espada¹, Lucía Alonso Virgós^2,*, Juan Luis Carús³, Miguel Ángel Fernández Fernández³

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.077447 - 13 August 2026

Abstract This research focuses on multi-object detection for interrelated industrial components, such as substation parts. Traditional vision models like YOLO rely primarily on visual features, which limits their ability to: (1) disambiguate visually similar objects using contextual cues like relative position or size, and (2) reinforce low-confidence detections that are spatially plausible given neighboring elements. For instance, a visually similar but semantically incorrect object may be misclassified, or a valid but partially occluded component may be discarded due to a low appearance-based score. Our approach addresses these issues by integrating formalized rules based on relative positions… More >