Yuanzhao Shang¹, Xin Liu^1,2,*, Fengbiao Zan¹

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084688 - 13 August 2026

Abstract Multivariate time-series anomaly detection is widely used to identify abnormal operating patterns in complex monitoring systems. Exogenous or contextual inputs can provide useful information for anomaly detection, but their effects on endogenous variables may appear with temporal delays. Direct synchronous modeling is therefore insufficient for capturing delayed response patterns. This study proposes EMS-uDTWAD, a multi-scale anomaly detection framework that combines explicit lag alignment with uncertainty-aware normal-pattern matching. First, the time-series data are divided into fine-grained and coarse-grained windows. At the coarse-grained scale, a lag alignment module estimates delayed responses from exogenous or contextual inputs to… More >

Ali Hassan¹, Syed Rizwan Hassan^2,*, Ammar Rafiq³

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084672 - 13 August 2026

Abstract Artificial Intelligence of Things (AIoT) systems have emerged through the rapid integration of artificial intelligence (AI) and the Internet of Things (IoT), enabling intelligent sensing, distributed learning, and real-time decision-making across diverse application domains. However, this convergence also introduces a significantly expanded adversarial attack surface spanning sensing devices, communication networks, learning pipelines, and actuation environments. This paper presents a comprehensive systematic review of adversarial threats and defence mechanisms in AIoT systems using a novel 3D-AIoT-TT (Three-Dimensional AIoT Threat Taxonomy) framework. The proposed taxonomy jointly models three fundamental dimensions: (i) AI pipeline stages, (ii) IoT architectural… More >

Ying Yan¹, Yongqiang Yang², Cong Jiang³, Bin Suo³, Kai Sun^4,*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084652 - 13 August 2026

Abstract Degradation data in practical reliability engineering are often scarce and heterogeneous, originating from multiple sources with varying degrees of uncertainty and conflict. Accordingly, this study proposes a hybrid framework that integrates Dempster–Shafer (D-S) evidence theory with the Wiener process for small-sample reliability assessment using multi-source heterogeneous data. First, a probabilistic non-uniform sampling method regularizes varied data sources and computes basic probability assignments (BPA). Second, a weight synthesis mechanism is constructed, where prior weights derived from prior knowledge are updated by evidence similarity quantified through the Expectation–Width (EW) distance, yielding posterior weights. Quantile sequences from each More >

Maher Alharby^1,*, Ali Alssaiari^2,3

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084639 - 13 August 2026

Abstract Large Language Models (LLMs) are increasingly being used to generate source code. However, a substantial proportion of their output contains security vulnerabilities. Existing defenses typically apply uniform analysis to all code fragments, irrespective of their risk profiles. This study presents RAVE-Code, a three-layer framework that calibrates the verification effort based on the risk associated with each detected weakness. The Detection layer employs Bandit for pattern-based static analysis, annotating findings with their respective Common Weakness Enumeration (CWE) classes. The Risk Scoring layer calculates a composite risk score for each weakness instance by integrating the Common Vulnerability… More >

Sofia Terzi^1,2,*, Katerina Zourou³, Ioannis Stamelos¹, Konstantinos Votis⁴

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084524 - 13 August 2026

Abstract Higher Education (HE) institutions and Lifelong Learning (LLL) providers increasingly issue digital certificates, yet prevailing solutions often lack interoperable credential schemas, verifiable provenance, and privacy-preserving verification at scale. In parallel, European initiatives promote verifiable credentials and cross-border recognition, but there is limited evidence on how Hyperledger Indy components—Redundant Byzantine Fault Tolerance (RBFT) consensus, Decentralized Identifiers (DIDs), Anonymous Credentials (AnonCreds), and revocation registries—can be integrated into existing learning platforms while satisfying software service-quality and governance requirements. This paper presents a permissioned, privacy-preserving blockchain architecture for secure issuance and verification of educational verifiable credentials (VCs) and evaluates… More >

Yuwei Lu^1,2, Jia Liu^1,2,*, Qiya Wang^1,2, Yujie Liu^1,2, Peng Luo^1,2

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084506 - 13 August 2026

Abstract Existing deep-learning-based steganography methods are typically designed for single-modality cover data and often rely on modality-specific network structures, which limits their cross-modal adaptability. To address this limitation, this paper proposes a multimodal implicit neural representation (INR) steganographic framework based on a point-cloud intermediate representation. The framework first fits the cover data as a carrier INR and samples the fitted carrier into a noisy point cloud. A pre-shared noise seed and secret key are then used to reproduce the carrier-derived point cloud and select a key-dependent point subset as the secret point cloud. Finally, a separate… More >

Guan Yang¹, Shiyan Kang¹, Bo Chen^2,3, Yu Wang^4,*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084499 - 13 August 2026

Abstract Web honeypots serve as foundational technologies for active deception, attracting attackers and extracting actionable threat intelligence. To address the challenges associated with manual and labor-intensive frontend construction, this paper presents the HFG framework, a security-oriented frontend generation framework designed for the large-scale deployment of heterogeneous Web-service decoy nodes. HFG utilizes a vision-to-code architecture integrating a PVT-CoT visual encoder, multi-scale adaptive fusion, visual token compression, a visual prefix bridge, and a Qwen2-LoRA code decoder. The model is trained on WebSight-derived data and evaluated on both the WebSight-derived test set and the Design2Code benchmark. General reconstruction metrics,… More >

Ye Lu¹, Haoyang Hu^1,*, Wenyi Chang¹, Wanbin Liu¹, Wenyuan Zhang²

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084490 - 13 August 2026

Abstract Fine-grained multiclass intrusion detection over flow-level traffic remains difficult, largely because class boundaries are often entangled, temporal dependence is non-negligible, and the label distribution is heavily long-tailed. In this study, a compact temporal convolutional network (TCN)-bidirectional gated recurrent unit (BiGRU)-TinyTransformer framework is developed to bring these issues into a single modeling pipeline: the TCN branch focuses on short-range anomalous patterns, the BiGRU branch captures bidirectional temporal structure, and the TinyTransformer branch complements them with broader contextual interaction learning. To reduce the bias induced by extreme imbalance, training is not driven by a single correction mechanism, More >

Ping Ma^1,2, Quan Wang^1,2, Yiyang Chen^3,*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084488 - 13 August 2026

Abstract Connected vehicles operating in V2X-enabled intelligent transportation systems often perform repetitive trajectory tracking in repeated tasks. In practical applications, traffic conditions, communication quality, and sensing accuracy may vary from trial to trial. These variations induce time-varying dynamics across repeated runs and reduce the effectiveness of iterative learning control (ILC) schemes when fixed or inaccurately identified models are used. To address this issue, this paper proposes a parameter-estimation-based ILC framework for connected vehicles. Parameter estimation is integrated with a norm-optimal ILC design through an expectation-maximization strategy. The time-varying model parameters and the learning input are updated More >

Ren-Jieh Kuo^1,*, Muhammad Rizki¹, Ferani Eva Zulvia², Eddy Roflin³

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084436 - 13 August 2026

Abstract Imbalanced data remain a critical challenge in classification, as skewed distributions bias models toward majority classes and diminish sensitivity to minority classes, which are often the most critical. To address this issue, this paper proposes the Information Filtered Hybrid Algorithm (IF-HA), a novel entropy-based sampling method that integrates undersampling and oversampling guided by information theory. IF-HA quantifies instance importance through an instance-wise difference statistic. In the undersampling stage, majority of instances with low difference statistics in the border area are eliminated, while in the oversampling stage, synthetic samples are generated from two minority core points… More >