Isam Bahaa Aldallal¹, Abdullahi Abdu Ibrahim^1,*, Saadaldeen Rashid Ahmed^2,3

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.075212 - 10 February 2026

Abstract The rapid growth of IoT networks necessitates efficient Intrusion Detection Systems (IDS) capable of addressing dynamic security threats under constrained resource environments. This paper proposes a hybrid IDS for IoT networks, integrating Support Vector Machine (SVM) and Genetic Algorithm (GA) for feature selection and parameter optimization. The GA reduces the feature set from 41 to 7, achieving a 30% reduction in overhead while maintaining an attack detection rate of 98.79%. Evaluated on the NSL-KDD dataset, the system demonstrates an accuracy of 97.36%, a recall of 98.42%, and an F1-score of 96.67%, with a low false More >

Alaa Tolah^*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074997 - 10 February 2026

Abstract The rapidly evolving cybersecurity threat landscape exposes a critical flaw in traditional educational programs where static curricula cannot adapt swiftly to novel attack vectors. This creates a significant gap between theoretical knowledge and the practical defensive capabilities needed in the field. To address this, we propose TeachSecure-CTI, a novel framework for adaptive cybersecurity curriculum generation that integrates real-time Cyber Threat Intelligence (CTI) with AI-driven personalization. Our framework employs a layered architecture featuring a CTI ingestion and clustering module, natural language processing for semantic concept extraction, and a reinforcement learning agent for adaptive content sequencing. By… More >

Yunhao Yu¹, Fuhua Luo¹, Zhenyong Zhang^2,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074277 - 10 February 2026

Abstract This paper investigates the detection and mitigation of coordinated cyberattacks on Load Frequency Control (LFC) systems integrated with Battery Energy Storage Systems (BESS). As renewable energy sources gain greater penetration, power grids are becoming increasingly vulnerable to cyber threats, potentially leading to frequency instability and widespread disruptions. We model two significant attack vectors: load-altering attacks (LAAs) and false data injection attacks (FDIAs) that corrupt frequency measurements. These are analyzed for their impact on grid frequency stability in both linear and nonlinear LFC models, incorporating generation rate constraints and nonlinear loads. A coordinated attack strategy is… More >

Tomás Bernal-Beltrán¹, Ronghao Pan¹, José Antonio García-Díaz¹, María del Pilar Salas-Zárate², Mario Andrés Paredes-Valverde², Rafael Valencia-García^1,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073629 - 10 February 2026

Abstract The malicious dissemination of hate speech via compromised accounts, automated bot networks and malware-driven social media campaigns has become a growing cybersecurity concern. Automatically detecting such content in Spanish is challenging due to linguistic complexity and the scarcity of annotated resources. In this paper, we compare two predominant AI-based approaches for the forensic detection of malicious hate speech: (1) fine-tuning encoder-only models that have been trained in Spanish and (2) In-Context Learning techniques (Zero- and Few-Shot Learning) with large-scale language models. Our approach goes beyond binary classification, proposing a comprehensive, multidimensional evaluation that labels each… More >

Qasem Abu Al-Haija^1,*, Shahad Al Tamimi²

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073540 - 10 February 2026

Abstract Adversarial Reinforcement Learning (ARL) models for intelligent devices and Network Intrusion Detection Systems (NIDS) improve system resilience against sophisticated cyber-attacks. As a core component of ARL, Adversarial Training (AT) enables NIDS agents to discover and prevent new attack paths by exposing them to competing examples, thereby increasing detection accuracy, reducing False Positives (FPs), and enhancing network security. To develop robust decision-making capabilities for real-world network disruptions and hostile activity, NIDS agents are trained in adversarial scenarios to monitor the current state and notify management of any abnormal or malicious activity. The accuracy and timeliness of… More >

Fatima Al-Quayed^*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.074799 - 29 January 2026

Abstract The rapid proliferation of Internet of Things (IoT) devices in critical healthcare infrastructure has introduced significant security and privacy challenges that demand innovative, distributed architectural solutions. This paper proposes FE-ACS (Fog-Edge Adaptive Cybersecurity System), a novel hierarchical security framework that intelligently distributes AI-powered anomaly detection algorithms across edge, fog, and cloud layers to optimize security efficacy, latency, and privacy. Our comprehensive evaluation demonstrates that FE-ACS achieves superior detection performance with an AUC-ROC of 0.985 and an F1-score of 0.923, while maintaining significantly lower end-to-end latency (18.7 ms) compared to cloud-centric (152.3 ms) and fog-only (34.5… More >

Sajid Ali¹, Qazi Mazhar Ul Haq^1,2,*, Ala Saleh Alluhaidan^3,*, Muhammad Shahid Anwar⁴, Sadique Ahmad⁵, Leila Jamel³

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2026.074395 - 29 January 2026

Abstract Spam emails remain one of the most persistent threats to digital communication, necessitating effective detection solutions that safeguard both individuals and organisations. We propose a spam email classification framework that uses Bidirectional Encoder Representations from Transformers (BERT) for contextual feature extraction and a multiple-window Convolutional Neural Network (CNN) for classification. To identify semantic nuances in email content, BERT embeddings are used, and CNN filters extract discriminative n-gram patterns at various levels of detail, enabling accurate spam identification. The proposed model outperformed Word2Vec-based baselines on a sample of 5728 labelled emails, achieving an accuracy of 98.69%, More >

Kwabena Owusu-Mensah^*, Edward Danso Ansong , Kofi Sarpong Adu-Manu, Winfred Yaokumah

Journal of Cyber Security, Vol.8, pp. 33-92, 2026, DOI:10.32604/jcs.2026.074265 - 20 January 2026

Abstract The rapid growth of digital payment systems and remote financial services has led to a significant increase in Card-Not-Present (CNP) fraud, which is now the primary source of card-related losses worldwide. Traditional rule-based fraud detection methods are becoming insufficient due to several challenges, including data imbalance, concept drift, privacy concerns, and limited interpretability. In response to these issues, a systematic review of twenty-four CNP fraud detection frameworks developed between 2014 and 2025 was conducted. This review aimed to identify the technologies, strategies, and design considerations necessary for adaptive solutions that align with evolving regulatory standards.… More >

Fatemeh Ahmadi Abkenari^*, Melika Zandi, Shanmugapriya Gopalakrishnan

Journal of Cyber Security, Vol.8, pp. 93-109, 2026, DOI:10.32604/jcs.2026.074197 - 20 January 2026

Abstract Nowadays, cyberattacks are considered a significant threat not only to the reputation of organizations through the theft of customers’ data or reducing operational throughput, but also to their data ownership and the safety and security of their operations. In recent decades, machine learning techniques have been widely employed in cybersecurity research to detect various types of cyberattacks. In the domain of cybersecurity data, and especially in Trojan detection datasets, it is common for datasets to record multiple statistical measures for a single concept. We referred to them as SWMF features in this paper, which include… More >

Sushruta Mishra¹, Sunil Kumar Mohapatra², Kshira Sagar Sahoo³, Anand Nayyar⁴, Tae-Kyung Kim^5,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072493 - 12 January 2026

Abstract With an increase in internet-connected devices and a dependency on online services, the threat of Distributed Denial of Service (DDoS) attacks has become a significant concern in cybersecurity. The proposed system follows a multi-step process, beginning with the collection of datasets from different edge devices and network nodes. To verify its effectiveness, experiments were conducted using the CICDoS2017, NSL-KDD, and CICIDS benchmark datasets alongside other existing models. Recursive feature elimination (RFE) with random forest is used to select features from the CICDDoS2019 dataset, on which a BiLSTM model is trained on local nodes. Local models… More >