Óscar Mogollón-Gutiérrez^*, José Carlos Sancho Núñez, Mar Ávila, MohammadHossein Homaei, Andrés Caro

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.079694 - 08 May 2026

Abstract Cyberattacks have increased in frequency and complexity in recent years, resulting in significant consequences for organizations. The negative consequences of cyberattacks force organizations to implement adequate cybersecurity measures to prevent and mitigate the impact of these attacks. Analysis of network traffic is essential for determining whether a cyberattack has been conducted. Intrusion detection systems (IDS) are used to detect malicious actions or irregularities in information systems. In conjunction with artificial intelligence (AI), they enable the development of intelligent intrusion detection systems. This paper presents an intelligent method of network traffic classification for securing systems with… More >

Xingyun Hu^1,2, Siqi Lu^1,2,*, Liujia Cai^1,2, Ye Feng^1,2, Shuhao Gu^1,2, Tao Hu¹, Yongjuan Wang^1,2,*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.079484 - 08 May 2026

Abstract With the widespread adoption of web applications and cloud services, the OAuth 2.0-based OpenID Connect (OIDC) Single Sign-on (SSO) protocol has become the core of modern digital identity authentication. Although the OIDC protocol itself has strict security specifications, its implementation in real-world web frameworks can introduce critical vulnerabilities, particularly the improper omission of the state parameter, which leads to severe authentication forgery risks. Existing research often overlooks these implementation-level flaws, especially from a formal analysis perspective. This paper addresses this gap by formally analyzing the authentication forgery attack resulting from the missing state parameter. We construct… More >

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 >

Sarmad Dheyaa Azeez¹, Saadaldeen Rashid Ahmed^2,3, Muhammad Ilyas^4,*, Abu Saleh Musa Miah⁵, Fahmid Al Farid^6,7,*, Md. Hezerul Abdul Karim^6,*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.076283 - 08 May 2026

Abstract Traditional Intrusion Detection Systems (IDSs) that rely on fixed signatures or basic machine learning often struggle with sophisticated, multi-stage cyberattacks and previously unknown threats. To fix these problems, this paper introduces IntrusionNet, a mixed deep learning system that combines Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Autoencoders in a two-part design. Differing from typical stacked models, IntrusionNet works on two levels at the same time. First, a supervised CNN-RNN process pulls spatial-temporal data from traffic flows to sort well-known attack patterns. Second, an unsupervised Autoencoder process spots new anomalies by looking at reconstruction… More >

Wajdan Al Malwi^1,*, Fatima Asiri¹, Muhammad Shahbaz Khan^2,3,*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.075326 - 08 May 2026

Abstract Modern intrusion detection systems (IDS) struggle to recognise zero-day cyberattacks, as classical discriminative models rely on historical attack labels and fail to characterise deviations from normal network behaviour. This work presents a hybrid quantum–classical intrusion detection framework in which a Quantum Circuit Born Machine (QCBM) models benign traffic as a probabilistic quantum state. The trained QCBM assigns each network flow a Quantum Anomaly Score (QAS), defined as the negative log-likelihood under the learned benign distribution, which is subsequently fused with classical flow statistics in a Light Gradient Boosted Machine (LightGBM) classifier. The proposed system employs a… More >

Maram Fahaad Almufareh^1,*, Mamoona Humayun², Sadia Din^3,*, Khalid Haseeb⁴, Amr Munshi⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.080287 - 27 April 2026

Abstract The real-time systems perform key functionalities in various fields to automate the communication and response in critical events. The Internet of Things (IoT), integrated with numerous physical objects, gathers environmental data, processes it at the edge, and provides intelligent decisions while routing health records to processing units. However, the dynamic and resource-constrained nature of IoT-based healthcare environments introduces significant challenges related to latency, transmission costs, and the reliable interaction of devices amid uncertain activities. In this work, we propose a framework for a consistent and trustworthy system that uses a weighted trust aggregation model to 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 >

Benedict Djouboussi^1,*, Elie Fute Tagne^1,2

Journal of Cyber Security, Vol.8, pp. 171-187, 2026, DOI:10.32604/jcs.2026.079617 - 15 April 2026

Abstract The Distributed Network Protocol 3 (DNP3) is widely deployed in SCADA-based microgrids; however, it was not originally designed to meet the cybersecurity requirements of modern decentralized energy infrastructures. Although DNP3 Secure Authentication (DNP3-SA) introduces HMAC-based session-level protection, it does not ensure fragment-level integrity, leaving the protocol vulnerable to fragmentation disruption, replay attacks, and sequence manipulation. Such vulnerabilities can cause desynchronization between master and outstation devices, compromising the operational reliability of distributed energy resources. This paper proposes DNP3Chain, a blockchain-enabled framework that provides real-time fragment-level validation and enforces end-to-end message integrity in DNP3 communications. An OpenDNP3-based… More >

Gangtao Han¹, Yurui Chen¹, Song Wang^1,*, Enqing Chen¹, Lingling Li², Gaofeng Pan³

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077768 - 09 April 2026

Abstract The integrity of perception data transmitted over in-vehicle networks is important for the safety of autonomous driving. However, legacy protocols like the Controller Area Network (CAN) bus which lacks essential security features make In-Vehicle Networks (IVNs) vulnerable to data tampering attacks. Current research typically focuses on detecting the attack itself but ignores the information recovery from the missing data, leading to an unsafe autonomous driving system. To address the issue, we propose a 3D object recovery framework to recover the missing data caused by the tampering attack that occurred in in-vehicle networks. The proposed framework… More >

Abdulaziz A. Alsulami^1,*, Badraddin Alturki², Ahmad J. Tayeb², Rayan A. Alsemmeari², Raed Alsini¹

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076413 - 09 April 2026

Abstract Intrusion Detection Systems (IDS) play a critical role in protecting networked environments from cyberattacks. They have become increasingly important in smart environments such as the Internet of Things (IoT) systems. However, IDS for IoT networks face critical challenges due to hardware constraints, including limited computational resources and storage capacity, which lead to high feature dimensionality, prediction uncertainty, and increased processing cost. These factors make many conventional detection approaches unsuitable for real-time IoT deployment. To address these challenges, this paper proposes an adaptive intrusion detection framework that intelligently balances detection accuracy and computational efficiency. The proposed… More >