Marwan Ali Albahar^*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.079941 - 08 May 2026

Abstract Advanced Persistent Threats (APTs) are stealthy cyberattacks that can evade detection in system-level audit logs. Provenance graphs encode these logs as interacting entities and events, exposing a causal and dependency structure that is often obscured in linear representations. Prior provenance-based detectors typically apply anomaly detection over such graphs, yet they frequently incur high false-positive rates and produce coarse grained alerts; moreover, approaches that heavily depend on node-specific identifiers (e.g., file paths) can learn spurious correlations, reducing robustness and limiting reliability across heterogeneous workloads. In this paper, we present Self-Training Adaptive Graph Encoder (stage), a lightweight, self-supervised… More >

Raza Hasan^*, Shakeel Ahmad, Ismet Gocer, Zakirul Bhuiyan

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.079708 - 08 May 2026

Abstract Maritime Domain Awareness (MDA) is critical for global security and economic stability, yet it is increasingly challenged by sophisticated adversarial tactics such as signal spoofing and “dark vessel” activities. Traditional surveillance systems, often reliant on single-sensor modalities, are ill-equipped to handle these deceptive behaviors. To address this, we propose the Multimodal Attention-based Fusion Transformer (MAFT), a novel deep learning architecture that integrates four distinct data modalities—Aerial imagery, Synthetic Aperture Radar (SAR), acoustic signatures, and Automatic Identification System (AIS) data—to achieve robust and interpretable maritime anomaly detection. A key contribution of our work is a principled… More > Graphic Abstract

Kelan Wang¹, Jianfei Chen^2,*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.079644 - 08 May 2026

Abstract As 6G-enabled Industrial Internet of Things (IIoT) evolves, green and sustainable industrial monitoring increasingly relies on edge AI to deliver low-latency diagnosis under tight resource constraints. Industrial cyber–physical systems increasingly rely on heterogeneous sensing and communication infrastructures, where network-side attacks can propagate into physical processes and appear as coupled anomalies. Reliable diagnosis therefore requires joint learning from time-synchronized cyber and physical telemetry rather than modeling them as independent signals. This paper develops Cyber–Physical Symbiosis Network (CPSNet), a model designed for edge-AI deployment with a dual-stream architecture for fixed-window multiclass cross-domain anomaly diagnosis in IIoT. CPSNet… More >

Chin Ta Wu^1,2, Shing Han Li^3,*, Ching Shih Tsou⁴

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.078762 - 08 May 2026

Abstract In semiconductor packaging processes, the wire bonding procedure, which connects chips to substrate lead frames using metal wires, is a crucial step. The quality of the bonding joints significantly affects product performance, including signal integrity and reliability, and is challenging to verify after subsequent processes. To mitigate the risk of defective bonding joints entering the assembly packaging stages of production, this study integrates the concepts of Fault Detection and Classification (FDC) and machine learning into the wire bonding process for enhanced anomaly detection. Production data from the machines were collected and analyzed using statistical methods More >

Haodong Zou^1,*, Yichen Zhao¹, Xin Chen¹, Ling Wang¹, Jinghang Yu¹, Long Yuan^2,*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.077908 - 08 May 2026

Abstract Root cause analysis (RCA), which leverages multi-modal observability data (including metrics, traces, and logs) to identify the fundamental source of system failures, is critical for ensuring the reliability of complex microservice systems. Traditionally, RCA has relied on human engineers to manually correlate these fragmented signals, which is a labor-intensive and error-prone process. Although recent AIOps advancements, particularly those leveraging Large Language Models (LLMs), aim to automate this workflow, they remain constrained by limitations. Existing methods often rely on single-modal data, restricting diagnostic comprehensiveness. Furthermore, approaches that utilize multi-modal data typically depend on simplistic temporal alignment,… More >

Xi Li¹, Yingjie Chang¹, Peng Chen^1,*, Ang Bian¹, Ning Lu^1,2,*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.077521 - 08 May 2026

Abstract Multivariate time series anomaly detection (MTSAD) is a critical task for real-time risk control and fault diagnosis in industrial monitoring, aerospace, and financial domains. Unsupervised MTSAD confronts three core challenges: label scarcity in practical scenarios, diverse anomaly patterns that demand adaptive modeling, and weak feature discriminability between normal and anomalous samples. To address these challenges, we propose a Prototype Memory and Contrastive Learning Based Unsupervised Anomaly Detection for Multivariate Time Series method named PC-UAD. PC-UAD comprises three core modules with hierarchical functionalities: (1) A Temporal PatchEmbedder, which adopts learnable positional encoding for dynamic temporal representation… More >

Shoeb Ali Syed¹, Zohaib Mushtaq^2,*, Akbare Yaqub³, Saifur Rahman⁴, Muhammad Irfan⁴, Saleh Al Dawsari^4,5,*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.076869 - 08 May 2026

Abstract Blockchain networks are under mounting pressure from emerging complex zero-day attacks that cannot be prevented with conventional security measures. In this paper, we introduce NeuroChain Sentinel, a new bio-inspired cybersecurity model based on spiking neural networks for detecting anomalies in a distributed ledger system in real time. The main innovations are: a Temporal Spike Pattern Recognition algorithm for simulating the biological timing of the neural system to detect malicious transaction patterns; a distributed consensus-verification topology combined with blockchain algorithms; and small-scale neuromorphic engineering, resulting in an 87% reduction in computational load over conventional deep neural… More >

Sarmad Dheyaa Azeez¹, Saadaldeen Rashid Ahmed^2,3, Muhammad Ilyas^4,*, Abu Saleh Musa Miah⁵, Fahmid Al Farid^6,7,*, Md. Hezerul Abdul Karim^6,*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.076283 - 08 May 2026

Abstract Traditional Intrusion Detection Systems (IDSs) that rely on fixed signatures or basic machine learning often struggle with sophisticated, multi-stage cyberattacks and previously unknown threats. To fix these problems, this paper introduces IntrusionNet, a mixed deep learning system that combines Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Autoencoders in a two-part design. Differing from typical stacked models, IntrusionNet works on two levels at the same time. First, a supervised CNN-RNN process pulls spatial-temporal data from traffic flows to sort well-known attack patterns. Second, an unsupervised Autoencoder process spots new anomalies by looking at reconstruction… More >

Wajdan Al Malwi^1,*, Fatima Asiri¹, Muhammad Shahbaz Khan^2,3,*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.075326 - 08 May 2026

Abstract Modern intrusion detection systems (IDS) struggle to recognise zero-day cyberattacks, as classical discriminative models rely on historical attack labels and fail to characterise deviations from normal network behaviour. This work presents a hybrid quantum–classical intrusion detection framework in which a Quantum Circuit Born Machine (QCBM) models benign traffic as a probabilistic quantum state. The trained QCBM assigns each network flow a Quantum Anomaly Score (QAS), defined as the negative log-likelihood under the learned benign distribution, which is subsequently fused with classical flow statistics in a Light Gradient Boosted Machine (LightGBM) classifier. The proposed system employs a… More >

Maram Fahaad Almufareh^1,*, Mamoona Humayun², Sadia Din^3,*, Khalid Haseeb⁴, Amr Munshi⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.080287 - 27 April 2026

Abstract The real-time systems perform key functionalities in various fields to automate the communication and response in critical events. The Internet of Things (IoT), integrated with numerous physical objects, gathers environmental data, processes it at the edge, and provides intelligent decisions while routing health records to processing units. However, the dynamic and resource-constrained nature of IoT-based healthcare environments introduces significant challenges related to latency, transmission costs, and the reliable interaction of devices amid uncertain activities. In this work, we propose a framework for a consistent and trustworthy system that uses a weighted trust aggregation model to More >