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Machine learning and Blockchain for AIoT: Robustness, Privacy, Trust and Security

Submission Deadline: 31 January 2026 View: 3297 Submit to Special Issue

Guest Editors

Prof. Jong Hyuk (James) Park (Leading Guest Editor), Seoul National University of Science and Technology, South Korea
Prof. Yi Pan, Georgia State University, USA
Prof. Ji Su Park, Jeonju University, South Korea

Summary

Artificial Intelligence of Things (AIoT) is considered a collaborative application of artificial intelligence (AI) and the Internet of Things (IoT). The AIoT system realizes real-time information acquisition through IoT sensors and performs intelligent data analysis tasks anywhere along the terminal-edge-cloud continuum, forming a smart and supportive ecosystem. However, AIoT systems face threats related to IoT data trust, system robustness, security, and privacy, making them susceptible to massive cyberattacks.

AI and blockchain ensure a secure environment for AIoT data communication, computation, and storage to solve trust, alertness, security, and privacy issues in AIoT. AI extends existing blockchain technology to bring a high level of economics, adaptability, and autonomy to blockchain systems. On top of existing blockchain technology, data mining, pattern recognition, machine learning, and deep learning can provide additional capabilities to blockchain systems, providing significant benefits to AIoT systems. Recently, it has been applied to cyber security, smart cities, smart grids, wireless sensor networks, mobile communications, crowdsourcing/crowd sensing, and cyber physical-social systems. However, AIoT's AI and blockchain technology still have several research problems and challenges.

Original papers are requested on topics of interest including, but not limited to:

1. Blockchain Theory and Algorithms for Robustness, Privacy, Trust and Security in AIoT

2. Machine Learning Theory and Algorithm for Robustness, Privacy, Trust and Security in AIoT

3. AI-based data analytics for AIoT

4. Machine/deep learning for AIoT

5. Secure AIoT system design based on ML and blockchain

6. Decentralized and collaborative learning for AIoT

7. Decentralized computing for AIoT(Robustness, Privacy, Trust and Security)

8. Big data analytics based on blockchain in AIoT systems

9. Performance optimization of blockchains in AIoT

Published Papers

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Open Access

ARTICLE

AI-Driven SDN and Blockchain-Based Routing Framework for Scalable and Trustworthy AIoT Networks
Mekhled Alharbi, Khalid Haseeb, Mamoona Humayun
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2601-2616, 2025, DOI:10.32604/cmes.2025.073039
（This article belongs to the Special Issue: Machine learning and Blockchain for AIoT: Robustness, Privacy, Trust and Security)
Abstract Emerging technologies and the Internet of Things (IoT) are integrating for the growth and development of heterogeneous networks. These systems are providing real-time devices to end users to deliver dynamic services and improve human lives. Most existing approaches have been proposed to improve energy efficiency and ensure reliable routing; however, trustworthiness and network scalability remain significant research challenges. In this research work, we introduce an AI-enabled Software-Defined Network (SDN)- driven framework to provide secure communication, trusted behavior, and effective route maintenance. By considering multiple parameters in the forwarder selection process, the proposed framework enhances network More >

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ARTICLE

Hybrid Meta-Heuristic Feature Selection Model for Network Traffic-Based Intrusion Detection in AIoT
Seungyeon Baek, Jueun Jeon, Byeonghui Jeong, Young-Sik Jeong
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 1213-1236, 2025, DOI:10.32604/cmes.2025.070679
（This article belongs to the Special Issue: Machine learning and Blockchain for AIoT: Robustness, Privacy, Trust and Security)
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 >

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299
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ARTICLE

MBID: A Scalable Multi-Tier Blockchain Architecture with Physics-Informed Neural Networks for Intrusion Detection in Large-Scale IoT Networks
Saeed Ullah, Junsheng Wu, Mian Muhammad Kamal, Heba G. Mohamed, Muhammad Sheraz, Teong Chee Chuah
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2647-2681, 2025, DOI:10.32604/cmes.2025.068849
（This article belongs to the Special Issue: Machine learning and Blockchain for AIoT: Robustness, Privacy, Trust and Security)
Abstract The Internet of Things (IoT) ecosystem faces growing security challenges because it is projected to have 76.88 billion devices by 2025 and $1.4 trillion market value by 2027, operating in distributed networks with resource limitations and diverse system architectures. The current conventional intrusion detection systems (IDS) face scalability problems and trust-related issues, but blockchain-based solutions face limitations because of their low transaction throughput (Bitcoin: 7 TPS (Transactions Per Second), Ethereum: 15–30 TPS) and high latency. The research introduces MBID (Multi-Tier Blockchain Intrusion Detection) as a groundbreaking Multi-Tier Blockchain Intrusion Detection System with AI-Enhanced Detection, which… More >

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ARTICLE

Port-Based Pre-Authentication Message Transmission Scheme
Sunghyun Yu, Yoojae Won
CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3943-3980, 2025, DOI:10.32604/cmes.2025.064997
（This article belongs to the Special Issue: Machine learning and Blockchain for AIoT: Robustness, Privacy, Trust and Security)
Abstract Pre-Authentication and Post-Connection (PAPC) plays a crucial role in realizing the Zero Trust security model by ensuring that access to network resources is granted only after successful authentication. While earlier approaches such as Port Knocking (PK) and Single Packet Authorization (SPA) introduced pre-authentication concepts, they suffer from limitations including plaintext communication, protocol dependency, reliance on dedicated clients, and inefficiency under modern network conditions. These constraints hinder their applicability in emerging distributed and resource-constrained environments such as AIoT and browser-based systems. To address these challenges, this study proposes a novel port-sequence-based PAPC scheme structured as a… More >

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ARTICLE

Defending against Backdoor Attacks in Federated Learning by Using Differential Privacy and OOD Data Attributes
Qingyu Tan, Yan Li, Byeong-Seok Shin
CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2417-2428, 2025, DOI:10.32604/cmes.2025.063811
（This article belongs to the Special Issue: Machine learning and Blockchain for AIoT: Robustness, Privacy, Trust and Security)
Abstract Federated Learning (FL), a practical solution that leverages distributed data across devices without the need for centralized data storage, which enables multiple participants to jointly train models while preserving data privacy and avoiding direct data sharing. Despite its privacy-preserving advantages, FL remains vulnerable to backdoor attacks, where malicious participants introduce backdoors into local models that are then propagated to the global model through the aggregation process. While existing differential privacy defenses have demonstrated effectiveness against backdoor attacks in FL, they often incur a significant degradation in the performance of the aggregated models on benign tasks.… More >

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ARTICLE

Privacy-Aware Federated Learning Framework for IoT Security Using Chameleon Swarm Optimization and Self-Attentive Variational Autoencoder
Saad Alahmari, Abdulwhab Alkharashi
CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 849-873, 2025, DOI:10.32604/cmes.2025.062549
（This article belongs to the Special Issue: Machine learning and Blockchain for AIoT: Robustness, Privacy, Trust and Security)
Abstract The Internet of Things (IoT) is emerging as an innovative phenomenon concerned with the development of numerous vital applications. With the development of IoT devices, huge amounts of information, including users’ private data, are generated. IoT systems face major security and data privacy challenges owing to their integral features such as scalability, resource constraints, and heterogeneity. These challenges are intensified by the fact that IoT technology frequently gathers and conveys complex data, creating an attractive opportunity for cyberattacks. To address these challenges, artificial intelligence (AI) techniques, such as machine learning (ML) and deep learning (DL),… More >

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600
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ARTICLE

LMSA: A Lightweight Multi-Key Secure Aggregation Framework for Privacy-Preserving Healthcare AIoT
Hyunwoo Park, Jaedong Lee
CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 827-847, 2025, DOI:10.32604/cmes.2025.061178
（This article belongs to the Special Issue: Machine learning and Blockchain for AIoT: Robustness, Privacy, Trust and Security)
Abstract Integrating Artificial Intelligence of Things (AIoT) in healthcare offers transformative potential for real-time diagnostics and collaborative learning but presents critical challenges, including privacy preservation, computational efficiency, and regulatory compliance. Traditional approaches, such as differential privacy, homomorphic encryption, and secure multi-party computation, often fail to balance performance and privacy, rendering them unsuitable for resource-constrained healthcare AIoT environments. This paper introduces LMSA (Lightweight Multi-Key Secure Aggregation), a novel framework designed to address these challenges and enable efficient, secure federated learning across distributed healthcare institutions. LMSA incorporates three key innovations: (1) a lightweight multi-key management system leveraging Diffie-Hellman… More >

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Machine learning and Blockchain for AIoT: Robustness, Privacy, Trust and Security

Guest Editors

Summary

Published Papers

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275

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80

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772

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299

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8458

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1491

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688

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381

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1639

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1039

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1836

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600

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1165

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557

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