Ahmed Murtaza¹, Abdullah Memon², Sana Hafeez³, Muzammil Ali², Ghulam E Mustafa Abro^4,*

Intelligent Automation & Soft Computing, Vol.41, pp. 49-72, 2026, DOI:10.32604/iasc.2026.082765 - 28 August 2026

Abstract Civilian Unmanned Aerial Systems (UAS) are increasingly deployed in smart-city monitoring, infrastructure inspection, logistics, and emergency response applications. However, their integration with wireless networks, cloud services, and AI-driven analytics significantly expands cybersecurity and privacy risks. Existing studies mainly focus on isolated technical vulnerabilities such as GNSS spoofing, jamming, and communication attacks, while lacking a unified framework that systematically connects cyber threats with quantitative privacy risk assessment. To address this research gap, this study proposes a layered threat-modeling framework for collaborative civilian UAS based on multidimensional attack-surface analysis and STRIDE-oriented threat mapping. In addition, a quantitative More >

Praise Emeka Nze^*, Adeniran Kolade Ademuwagun, Muktar Bello, Fortune Daberechi Ifeanyi, Samaila Musa Abdullahi, John Tighil

Journal of Cyber Security, Vol.8, pp. 469-486, 2026, DOI:10.32604/jcs.2026.083943 - 21 August 2026

Abstract The rapid evolution of adversarial cyber threats demands proactive, scalable security testing methodologies capable of producing realistic, organization-specific attack scenarios. Conventional approaches, including manual red-teaming, scripted Breach and Attack Simulation (BAS) platforms, and tabletop exercises, are constrained by high expert dependency, limited scenario variability, and an inability to dynamically adapt to an organization’s unique threat profile. This paper proposes and evaluates a Large Language Model (LLM)-Assisted Threat-Driven Testing System that integrates the MITRE Adversarial Tactics, Techniques, and Common Knowledge (MITRE ATT&CK) framework v14, a structured knowledge base of adversarial tactics, techniques, and procedures (TTPs), with… More >

Ali Hassan¹, Syed Rizwan Hassan^2,*, Ammar Rafiq³

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084672 - 13 August 2026

Abstract Artificial Intelligence of Things (AIoT) systems have emerged through the rapid integration of artificial intelligence (AI) and the Internet of Things (IoT), enabling intelligent sensing, distributed learning, and real-time decision-making across diverse application domains. However, this convergence also introduces a significantly expanded adversarial attack surface spanning sensing devices, communication networks, learning pipelines, and actuation environments. This paper presents a comprehensive systematic review of adversarial threats and defence mechanisms in AIoT systems using a novel 3D-AIoT-TT (Three-Dimensional AIoT Threat Taxonomy) framework. The proposed taxonomy jointly models three fundamental dimensions: (i) AI pipeline stages, (ii) IoT architectural… More >

Sayfudin Sayfudin^1,2, Deris Stiawan^3,*, Ferdiansyah Ferdiansyah⁴, Rahmat Budiarto⁵

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084177 - 13 August 2026

Abstract Tightening global regulation of digital toxicity demands hate-speech detection that is accurate, explainable, traceable, and forensically usable. The challenge intensifies in multilingual and code-mixed settings such as Indonesian social media, where linguistic variation and informal expressions cause feature sparsity and reduce machine learning (ML) effectiveness. Most prior work emphasizes text classification while neglecting actor profiling and the network structures through which hate speech propagates. We propose Dynamic Lexicon-Driven Network (DyLex-Net), an integrated framework for profiling actors who disseminate hate speech, combining dataset-driven dynamic-lexicon analysis, classical ML ensemble validation, and ego-network analysis under a forensic-readiness orientation.… More >

Tahani Alsubait^*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.083550 - 13 August 2026

Abstract The recent sophistication of contemporary cyber threats, such as advanced persistent threats (APTs), zero-day exploits, and polymorphic malware, has revealed serious limitations of traditional rule-based and shallow machine learning detection systems. This paper introduces a new self-managed cyber threat detection and response model, CyberSentinel-LLM, that leverages a fine-tuned large language model (LLM) and a multi-agent reinforcement learning system. The framework employs a LoRA-adapted LLaMA-3-8B backbone (fine-tuned on domain-specific cybersecurity log data using Low-Rank Adaptation with rank r = 16) for contextual log analysis, semantic threat classification, and automated incident response through four specialised agents: Detection,… More >

Malika Abid¹, Mohammed Kamel Benkaddour¹, Mohamed Benouis², Amine Khaldi¹, Monalisa Sahu³, Aditya Kumar Sahu^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.081055 - 27 July 2026

Abstract Federated Learning (FL) is a cutting-edge method in the medical imaging field that allows hospitals to collaboratively build models without revealing patient data. Nevertheless, FL is still vulnerable to numerous security and privacy issues, including, but not limited to, data poisoning, Byzantine attacks, and inference attacks. The existing literature has only partly dealt with this topic by focusing either on particular threats or on mitigation strategies, thus leaving the overall comprehension of the problems and their solutions in medical imaging as inadequate. The main threats to FL are systematically classified in this systematic review, with… More >

Saken Tleuberdin¹, Dina Satybaldina^2,*, Raikhan Muratkhan³, Gulsipat Abisheva^2,*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081906 - 23 July 2026

Abstract We perform a cross-layer penetration testing on one of the most popular Wi-Fi smart locks (Tuya 902V). The methodology combines wireless traffic analysis using an Alfa AWUS036AXML adapter, forced re-association via deauthentication to make Wi-Fi Protected Access 2 (WPA2) 4-way Extensible Authentication Protocol over LAN (EAPOL) handshake visible with Airodump/Aireplay, offline dictionary attack with Aircrack-ng, Android app reverse engineering using Apktool, Jadx, and MobSF; denial-of-service experiment (DoS) executed by hping3; Near-Field Communications (NFC)/Radio-Frequency Identification (RFID) key-clone attempt by Flipper Zero. Handshake is empirically captured but no Wi-Fi passphrase found under 14M dictionary entries; DoS test… More >

Rayan Alenzi, Rayan Aldoghan^*, M. M. Hafizur Rahman

Journal of Cyber Security, Vol.8, pp. 373-396, 2026, DOI:10.32604/jcs.2026.083953 - 01 July 2026

Abstract The rapid proliferation of Internet of Things (IoT) devices across smart homes, healthcare facilities, industrial networks, and smart cities has raised critical security concerns, particularly regarding device authentication. IoT devices are typically characterized by limited computational resources, constrained memory, and restricted energy budgets, which renders the deployment of traditional cryptographic protocols infeasible; consequently, lightweight authentication schemes are required. Although numerous lightweight authentication protocols have been proposed, a systematic risk evaluation of such protocols against established threat modeling frameworks remains largely absent from the existing literature. This paper presents a systematic literature review (SLR) based on… More >

Salman Khan^*, Mai Alzamel^*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.084282 - 30 June 2026

Abstract Traditional threat detection has proven ineffective in large-scale, moving data in the era of ever-more complex adversarial techniques and interconnected systems. The challenge becomes even more complex when high-volume, unstructured data continuously streams from social media platforms, requiring them to process the data efficiently and intelligently to provide timely security insights. Considering the big data security, the present study presents a scalable deep-learning-based system for real-time cyber threat detection, which has been developed and validated especially for distributed big data processing environments. A hybrid embedding approach that combines Word2Vec and Iterated Dilated Convolutional Neural Networks… More > Graphic Abstract

Yunkeun Song¹, Samuel Woo², Suji Lee³, Yousik Lee^3,*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.083267 - 15 June 2026

Abstract Software-Defined Vehicles (SDVs) increase cybersecurity complexity through the combination of external connectivity, software-intensive functions, and distributed development across vehicle manufacturers and suppliers. Although United Nations (UN) Regulation No. 155 and ISO/SAE 21434 require Threat Analysis and Risk Assessment (TARA) throughout the vehicle lifecycle, conventional TARA methodologies remain largely system-focused and often provide limited procedural guidance for coordinating supplier-derived TARA results at the vehicle level. This paper proposes an orchestration model for TARA across vehicle manufacturers and suppliers that structures TARA activities into the concept phase and the product development phases. The model defines interactions between… More >