Nader Karmous¹, Leila Bousbia¹, Mohamed Ould-Elhassen Aoueileyine¹, Imen Filali^2,*, Ridha Bouallegue¹

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.077454 - 08 May 2026

Abstract This study proposes an intelligent Intrusion Detection and Prevention System (IDPS) integrated into a centralized Ryu Software-Defined Networking (SDN) controller to mitigate replay attacks within Internet of Things (IoT) environments. To address the scarcity of specialized datasets, a comprehensive dataset was generated using a real-time SDN-IoT testbed encompassing Mininet, multiple OpenFlow 1.3 switches, and a single Ryu controller. The experimental setup featured the exchange of legitimate and malicious Message Queuing Telemetry Transport (MQTT) traffic between hosts and IoT devices to simulate realistic network behaviors and attack vectors. Our methodology introduces a novel feature engineering framework… More >

Liam Revell¹, Hyunjae Kang^1,*, Jung Taek Seo², Dan Dongseong Kim¹

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.078467 - 27 April 2026

Abstract The rapid proliferation of Internet of Things (IoT) devices has increased the importance of network intrusion detection systems (NIDS) for protecting modern networks. However, many machine learning and deep learning based NIDS rely on large volumes of labeled attack data, which is often impractical to obtain for newly emerging or rare attacks. This paper presents a benchmark-style systematic evaluation of meta-learning-based Few-Shot Learning (FSL) classifiers for detecting previously unseen intrusions with limited labeled data. We investigate three representative FSL models, namely Prototypical Networks, Relation Networks, and MetaOptNet, and further examine two decision-level ensemble strategies based… More >

Wajdan Al Malwi¹, Fatima Asiri¹, Nazik Alturki², Noha Alnazzawi³, Dimitrios Kasimatis⁴, Nikolaos Pitropakis^5,*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.074274 - 12 March 2026

Abstract The expansion of 5G-enabled Internet of Things (IoT) networks, while enabling transformative applications, significantly increases the attack surface and necessitates security solutions that extend beyond traditional intrusion detection. Existing intrusion detection systems (IDSs) mainly operate in an open-loop manner, excelling at classification but lacking the ability for autonomous, safety-aware remediation. This gap is particularly critical in 5G environments, where manual intervention is too slow and naive automation can lead to severe service disruptions. To address this issue, we propose a novel Self-Healing Intrusion Detection System (SH-IDS) framework that develops a closed-loop cyber defense mechanism. The… More >

Hafiz Burhan Ul Haq¹, Waseem Akram², Haroon ur Rashid Kayani³, Khalid Mahmood^4,*, Chihhsiong Shih⁵, Rupak Kharel^6,7, Amina Salhi⁸

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2025.073188 - 12 March 2026

Abstract The Internet of Things (IoT) is a new model that evolved with the rapid progress of advanced technology and gained tremendous popularity due to its applications. Anomaly detection has widely attracted researchers’ attention in the last few years, and its effects on diverse applications. This review article covers the various methods and tools developed to perform the task efficiently and automatically in a smart city. In this work, we present a comprehensive literature review (2011 onwards) of three major types of anomalies: network anomalies, sensor anomalies, and video-based anomalies, along with their methods and software… More >

Jian Wu^*, Xiaowei Hao, Chao Han

Energy Engineering, Vol.123, No.3, 2026, DOI:10.32604/ee.2025.071023 - 27 February 2026

Abstract Owing to the development of communication technologies and control systems, the integration of numerous Internet of Things (IoT) nodes into the power grid has become increasingly prevalent. These nodes are deployed to gather operational data from various distributed energy sources and monitor real-time energy consumption, thereby transforming the traditional power grid into a smart grid (SG). However, the openness of wireless communication channels introduces vulnerabilities, as it allows potential eavesdroppers to intercept sensitive information. This poses threats to the secure and efficient operation of the IoT-driven smart grid. To address these challenges, we propose a… More >

Enzo Hoummady^*, Fehmi Jaafar

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074897 - 10 February 2026

Abstract With the proliferation of Internet of Things (IoT) devices, securing these interconnected systems against cyberattacks has become a critical challenge. Traditional security paradigms often fail to cope with the scale and diversity of IoT network traffic. This paper presents a comparative benchmark of classic machine learning (ML) and state-of-the-art deep learning (DL) algorithms for IoT intrusion detection. Our methodology employs a two-phased approach: a preliminary pilot study using a custom-generated dataset to establish baselines, followed by a comprehensive evaluation on the large-scale CICIoTDataset2023. We benchmarked algorithms including Random Forest, XGBoost, CNN, and Stacked LSTM. The… More >

Mehran Tarif¹, Mohammadhossein Homaei^2,*, Abbas Mirzaei³, Babak Nouri-Moghaddam³

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.071676 - 10 February 2026

Abstract The Routing Protocol for Low-power and Lossy Networks (RPL) is widely used in Internet of Things (IoT) systems, where devices usually have very limited resources. However, RPL still faces several problems, such as high energy usage, unstable links, and inefficient routing decisions, which reduce the overall network performance and lifetime. In this work, we introduce TABURPL, an improved routing method that applies Tabu Search (TS) to optimize the parent selection process. The method uses a combined cost function that considers Residual Energy, Transmission Energy, Distance to the Sink, Hop Count, Expected Transmission Count (ETX), and More >

Muhammad Saad Farooqui¹, Aizaz Ahmad Khattak², Bakri Hossain Awaji³, Nazik Alturki⁴, Noha Alnazzawi⁵, Muhammad Hanif^6,*, Muhammad Shahbaz Khan²

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4395-4439, 2025, DOI:10.32604/cmes.2025.072023 - 23 December 2025

Abstract Quality of Service (QoS) assurance in programmable IoT and 5G networks is increasingly threatened by cyberattacks such as Distributed Denial of Service (DDoS), spoofing, and botnet intrusions. This paper presents AutoSHARC, a feedback-driven, explainable intrusion detection framework that integrates Boruta and LightGBM–SHAP feature selection with a lightweight CNN–Attention–GRU classifier. AutoSHARC employs a two-stage feature selection pipeline to identify the most informative features from high-dimensional IoT traffic and reduces 46 features to 30 highly informative ones, followed by post-hoc SHAP-guided retraining to refine feature importance, forming a feedback loop where only the most impactful attributes are More >

Mekhled Alharbi^1,*, Khalid Haseeb², Mamoona Humayun^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2601-2616, 2025, DOI:10.32604/cmes.2025.073039 - 26 November 2025

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 >

Saeed Ullah¹, Junsheng Wu^1,*, Mian Muhammad Kamal², Heba G. Mohamed³, Muhammad Sheraz⁴, Teong Chee Chuah^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2647-2681, 2025, DOI:10.32604/cmes.2025.068849 - 31 August 2025

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 >