Rayan Alenzi, Rayan Aldoghan^*, M. M. Hafizur Rahman

Journal of Cyber Security, Vol.8, pp. 373-396, 2026, DOI:10.32604/jcs.2026.083953 - 01 July 2026

Abstract The rapid proliferation of Internet of Things (IoT) devices across smart homes, healthcare facilities, industrial networks, and smart cities has raised critical security concerns, particularly regarding device authentication. IoT devices are typically characterized by limited computational resources, constrained memory, and restricted energy budgets, which renders the deployment of traditional cryptographic protocols infeasible; consequently, lightweight authentication schemes are required. Although numerous lightweight authentication protocols have been proposed, a systematic risk evaluation of such protocols against established threat modeling frameworks remains largely absent from the existing literature. This paper presents a systematic literature review (SLR) based on… More >

Lilia Tightiz¹, L. Minh Dang^2,3, Hyosik Yang^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.081849 - 30 June 2026

Abstract We present QFedFormer, a federated transformer for dynamic electric vehicle (EV)-charging price prediction that combines quantization-aware training, SHAP-guided explainability, and blockchain-based incentives. The framework trains across distributed charging stations without centralizing user data, and programmable contracts set tariffs from forecasted demand and user-declared flexibility, while token rewards are derived from SHAP-based utility scores and anchored on-chain via Merkle proofs. On a real-world dataset, QFedFormer attains an energy-demand RMSE of 1.82±0.02 kWh and a tariff RMSE of 11.83±0.10 KRW/kWh (MAPE 2.7±0.2%) in the non-private baseline, outperforming FedAvg and Block-FeDL by 14.1% and 9.5More > Graphic Abstract

Hong Min^1,*, Yousef Ibrahim Daradkeh², Jung Taek Seo^3,*, Mohd Anjum⁴, Sana Shahab⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.080871 - 30 June 2026

Abstract This study presents a computational modeling framework for efficient and secure computation offloading in Internet of Things (IoT)-enabled smart contract systems. The integration of IoT, edge computing, and blockchain introduces significant challenges, including limited device capacity, high verification cost, and scalability constraints. Existing blockchain verification approaches depend on computationally intensive cryptographic operations that are inefficient for resource-constrained IoT devices, resulting in increased latency, energy consumption, and transaction costs. To address these issues, this study proposes the Zero-Knowledge Fuzzy Logic Offloading and Rollup (Z-FLOR) framework, an adaptive and energy-efficient model designed to enable secure and verifiable… More >

Liangyu Lin, Li Feng^*, Lin Huang

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.083047 - 15 June 2026

Abstract The modern sports streaming market is severely fragmented, forcing fans into costly, siloed platforms. While blockchain-based decentralized architectures offer a unified, interoperable sport streaming ecosystem, securely delivering commercial video over untrusted infrastructure remains a profound cryptographic challenge. Existing schemes fail to simultaneously support highly granular on-demand highlights and large scale dynamic live subscriptions. To resolve this, we propose a novel decentralized authorization architecture that systematically integrates existing cryptographic primitives into a decoupled three-layer protocol. By securely bridging on-chain state transitions with off-chain cryptographic enforcement, our architecture directly maps commercial payment workflows onto the underlying key More >

Chao Zhang¹, Weidong Zhong¹, Xu An Wang¹, Weiwei Jiang^2,*, Ziteng Wang², Miao Tian¹, Jianhong Ling¹, Hangjiang Du¹, Yunhui Duan¹

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.081399 - 15 June 2026

Abstract The integrity risks posed by data outsourcing in cloud storage have driven the development of remote data integrity auditing (RDIA) technologies. However, traditional schemes rely on trusted third-party auditors (TPAs), leading to potential collusion and single-point failure vulnerabilities. The integration of blockchain alleviates these issues through decentralization and transparency, yet existing blockchain-based certificateless auditing schemes still suffer from security flaws in the tag generation phase. Addressing the tag forgery vulnerability in Miao et al.’s scheme, which stems from the absence of random parameters in the hash function input, this paper proposes a lightweight enhancement mechanism: More >

Bin Fang¹, Qi Yu¹, Han Wu², Xingxing Hou², Jingyu Zhang^2,*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.080210 - 15 June 2026

Abstract The integration of Internet of Things (IoT) with blockchain technology introduces significant challenges in handling massive and frequent transaction data generated by distributed IoT devices. The Unspent Transaction Output (UTXO) model, widely adopted in blockchain systems like Bitcoin, faces critical scalability issues when applied to IoT environments. This is because the datasets it processes expand rapidly, which consumes a large amount of memory and increases the disk access latency of resource-constrained IoT nodes. Existing optimization approaches exhibit limitations in dynamic adaptability and protocol compatibility. To address these challenges, we propose an improved blockchain-empowered storage service More >

Priyanka Halder, Gopikrishnan Sundaram^*

Journal on Internet of Things, Vol.8, pp. 127-153, 2026, DOI:10.32604/jiot.2026.073962 - 29 May 2026

Abstract The need for intelligent learning frameworks that can function under stringent limitations relating to privacy, energy, scalability, and trust has increased due to the Internet of Things’ (IoT) and the Internet of Artificial Things’ (IoAT) explosive expansion. Federated Learning (FL), which allows collaborative model training without sharing raw data, has become a potential approach. Non-IID data delivery, inconsistent client engagement, vulnerability to poisoning assaults, and low resource knowledge are among of the significant obstacles that FL alone must overcome. Blockchain integration adds extra overhead in terms of latency, energy consumption, and scalability, but it has… More >

Ji Su Park^1,*, Pan Yi², Jong Hyuk (James) Park³

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.083347 - 27 May 2026

Abstract This article has no abstract. More >

Ghadi Almaktoom, Suliman Aladhadh, Salim El Khediri^*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.078774 - 27 May 2026

Abstract The rapid growth of phishing attempts in the enterprise could potentially lead to bankruptcy. The primary focus of the research is on detecting phishing attacks, with no interest in how the data is processed. Attackers use fraudulent methods to obtain valuable, confidential information, resulting in billions of dollars in financial losses for enterprises. In our review, we examined the methods used in phishing-detection studies. We concluded that the two main sections, centralized and decentralized methods, were the centralized ones, which aggregate data in a central server and thus violate data protection regulations, such as GDPR.… More >

Mosammat Jannatul Kobra¹, Muhammad Rashid Majeed^2,*, Md Owahedur Rahman¹

Journal of Blockchain and Intelligent Computing, Vol.2, pp. 27-44, 2026, DOI:10.32604/jbic.2026.081492 - 13 May 2026

Abstract The proposed study introduces a blockchain-based framework for an adaptive security solution with real-time incident response and usability feedback for non-expert users. Traditional security solutions are often designed with static, opaque policies, which makes them complex. These issues make them less effective in dealing with complex environments. Thus, to make them more effective, the proposed framework introduces supervised machine learning for attack classification, unsupervised machine learning for anomaly detection, a risk-aware, adaptive policy engine, and a lightweight, tamper-evident, hash-linked ledger for auditable decision-making. The proposed framework uses a Random Forest classifier for BENIGN/ATTACK classification, and… More >