Abeer Alnuaim^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-33, 2026, DOI:10.32604/cmc.2025.069110 - 10 November 2025

Abstract The rapid digitalization of urban infrastructure has made smart cities increasingly vulnerable to sophisticated cyber threats. In the evolving landscape of cybersecurity, the efficacy of Intrusion Detection Systems (IDS) is increasingly measured by technical performance, operational usability, and adaptability. This study introduces and rigorously evaluates a Human-Computer Interaction (HCI)-Integrated IDS with the utilization of Convolutional Neural Network (CNN), CNN-Long Short Term Memory (LSTM), and Random Forest (RF) against both a Baseline Machine Learning (ML) and a Traditional IDS model, through an extensive experimental framework encompassing many performance metrics, including detection latency, accuracy, alert prioritization, classification… More >

Misbah Anwer^1,*, Ghufran Ahmed¹, Maha Abdelhaq², Raed Alsaqour³, Shahid Hussain⁴, Adnan Akhunzada^5,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-15, 2026, DOI:10.32604/cmc.2025.068673 - 10 November 2025

Abstract The exponential growth of the Internet of Things (IoT) has introduced significant security challenges, with zero-day attacks emerging as one of the most critical and challenging threats. Traditional Machine Learning (ML) and Deep Learning (DL) techniques have demonstrated promising early detection capabilities. However, their effectiveness is limited when handling the vast volumes of IoT-generated data due to scalability constraints, high computational costs, and the costly time-intensive process of data labeling. To address these challenges, this study proposes a Federated Learning (FL) framework that leverages collaborative and hybrid supervised learning to enhance cyber threat detection in… More >

Ehan Sohn¹, Sangmyung Lee¹, Sunggon Kim¹, Kiwook Sohn¹, Manish Kumar², Yongseok Son^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2761-2785, 2025, DOI:10.32604/cmes.2025.070888 - 26 November 2025

Abstract With the accelerated growth of the Internet of Things (IoT), real-time data processing on edge devices is increasingly important for reducing overhead and enhancing security by keeping sensitive data local. Since these devices often handle personal information under limited resources, cryptographic algorithms must be executed efficiently. Their computational characteristics strongly affect system performance, making it necessary to analyze resource impact and predict usage under diverse configurations. In this paper, we analyze the phase-level resource usage of AES variants, ChaCha20, ECC, and RSA on an edge device and develop a prediction model. We apply these algorithms… More >

Inam Ullah Khan¹, Fida Muhammad Khan^1,*, Zeeshan Ali Haider¹, Fahad Alturise^2,*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4307-4344, 2025, DOI:10.32604/cmc.2025.070189 - 23 October 2025

Abstract The rapid expansion of the Internet of Things (IoT) has introduced significant security challenges due to the scale, complexity, and heterogeneity of interconnected devices. The current traditional centralized security models are deemed irrelevant in dealing with these threats, especially in decentralized applications where the IoT devices may at times operate on minimal resources. The emergence of new technologies, including Artificial Intelligence (AI), blockchain, edge computing, and Zero-Trust-Architecture (ZTA), is offering potential solutions as it helps with additional threat detection, data integrity, and system resilience in real-time. AI offers sophisticated anomaly detection and prediction analytics, and… More >

Imran Ahmed^1,#, Misbah Ahmad^2,3,#, Gwanggil Jeon^4,5,*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4237-4273, 2025, DOI:10.32604/cmc.2025.068319 - 23 October 2025

Abstract The rapid convergence of Information and Communication Technologies (ICT), driven by advancements in 5G/6G networks, cloud computing, Artificial Intelligence (AI), and the Internet of Things (IoT), is reshaping modern digital ecosystems. As massive, distributed data streams are generated across edge devices and network layers, there is a growing need for intelligent, privacy-preserving AI solutions that can operate efficiently at the network edge. Federated Learning (FL) enables decentralized model training without transferring sensitive data, addressing key challenges around privacy, bandwidth, and latency. Despite its benefits in enhancing efficiency, real-time analytics, and regulatory compliance, FL adoption faces… More >

Hitesh Mohapatra^*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 221-247, 2025, DOI:10.32604/cmc.2025.068338 - 29 August 2025

Abstract Fault tolerance is essential for reliable and sustainable smart city infrastructure. Interconnected IoT systems must function under frequent faults, limited resources, and complex conditions. Existing research covers various fault-tolerant methods. However, current reviews often lack system-level critique and multidimensional analysis. This study provides a structured review of fault tolerance strategies across layered IoT architectures in smart cities. It evaluates fault detection, containment, and recovery techniques using specific metrics. These include fault visibility, propagation depth, containment score, and energy-resilience trade-offs. The analysis uses comparative tables, architecture-aware discussions, and conceptual plots. It investigates the impact of fault… More >

Qamar H. Naith^1,*, H. Mancy^2,3

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 387-405, 2025, DOI:10.32604/cmes.2025.066917 - 31 July 2025

Abstract Effective water distribution and transparency are threatened with being outrightly undermined unless the good name of urban infrastructure is maintained. With improved control systems in place to check leakage, variability of pressure, and conscientiousness of energy, issues that previously went unnoticed are now becoming recognized. This paper presents a grandiose hybrid framework that combines Multi-Agent Deep Reinforcement Learning (MADRL) with Shapley Additive Explanations (SHAP)-based Explainable AI (XAI) for adaptive and interpretable water resource management. In the methodology, the agents perform decentralized learning of the control policies for the pumps and valves based on the real-time… More >

Radhika Kumari¹, Kiranbir Kaur¹, Ahmad Almogren², Ayman Altameem³, Salil Bharany^4,*, Yazeed Yasin Ghadi⁵, Ateeq Ur Rehman^6,*

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5509-5525, 2025, DOI:10.32604/cmc.2025.064247 - 30 July 2025

Abstract The exponential growth of the Internet of Things (IoT) has revolutionized various domains such as healthcare, smart cities, and agriculture, generating vast volumes of data that require secure processing and storage in cloud environments. However, reliance on cloud infrastructure raises critical security challenges, particularly regarding data integrity. While existing cryptographic methods provide robust integrity verification, they impose significant computational and energy overheads on resource-constrained IoT devices, limiting their applicability in large-scale, real-time scenarios. To address these challenges, we propose the Cognitive-Based Integrity Verification Model (C-BIVM), which leverages Belief-Desire-Intention (BDI) cognitive intelligence and algebraic signatures to… More >

Yu-Bin Kim, Dong-Hyuk Shin, Ieck-Chae Euom^*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2217-2243, 2025, DOI:10.32604/cmc.2025.063289 - 03 July 2025

Abstract The global surge in electric vehicle (EV) adoption is proportionally expanding the EV charging station (EVCS) infrastructure, thereby increasing the attack surface and potential impact of security breaches within this critical ecosystem. While ISO 15118 standardizes EV-EVCS communication, its underspecified security guidelines and the variability in manufacturers’ implementations frequently result in vulnerabilities that can disrupt charging services, compromise user data, or affect power grid stability. This research introduces a systematic black-box fuzzing methodology, accompanied by an open-source tool, to proactively identify and mitigate such security flaws in EVCS firmware operating under ISO 15118. The proposed… More >

Alireza Bahmani^*

Journal on Internet of Things, Vol.7, pp. 1-17, 2025, DOI:10.32604/jiot.2025.060786 - 24 June 2025

Abstract With the development of the Internet of Things (IoT) technology and its widespread integration in various aspects of life, the risks associated with cyberattacks on these systems have increased significantly. Vulnerabilities in IoT devices, stemming from insecure designs and software weaknesses, have made attacks on them more complex and dangerous compared to traditional networks. Conventional intrusion detection systems are not fully capable of identifying and managing these risks in the IoT environment, making research and evaluation of suitable intrusion detection systems for IoT crucial. In this study, deep learning, multi-layer perceptron (MLP), Random Forest (RF),… More >