Kirubavathi Ganapathiyappan^1,*, Kiruba Marimuthu Eswaramoorthy¹, Abi Thangamuthu Shanthamani¹, Aksaya Venugopal¹, Asita Pon Bhavya Iyyappan¹, Thilaga Manickam¹, Ateeq Ur Rehman^2,*, Habib Hamam^3,4,5,6

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-31, 2026, DOI:10.32604/cmc.2025.070067 - 09 December 2025

Abstract The growing use of Portable Document Format (PDF) files across various sectors such as education, government, and business has inadvertently turned them into a major target for cyberattacks. Cybercriminals take advantage of the inherent flexibility and layered structure of PDFs to inject malicious content, often employing advanced obfuscation techniques to evade detection by traditional signature-based security systems. These conventional methods are no longer adequate, especially against sophisticated threats like zero-day exploits and polymorphic malware. In response to these challenges, this study introduces a machine learning-based detection framework specifically designed to combat such threats. Central to… More >

Fatima Al-Quayed^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-16, 2026, DOI:10.32604/cmc.2025.069102 - 10 November 2025

Abstract Due to the growth of smart cities, many real-time systems have been developed to support smart cities using Internet of Things (IoT) and emerging technologies. They are formulated to collect the data for environment monitoring and automate the communication process. In recent decades, researchers have made many efforts to propose autonomous systems for manipulating network data and providing on-time responses in critical operations. However, the widespread use of IoT devices in resource-constrained applications and mobile sensor networks introduces significant research challenges for cybersecurity. These systems are vulnerable to a variety of cyberattacks, including unauthorized access,… 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 >

Hamed Alqahtani¹, Gulshan Kumar^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 1439-1479, 2025, DOI:10.32604/cmes.2025.067738 - 31 August 2025

Abstract Domain Generation Algorithms (DGAs) continue to pose a significant threat in modern malware infrastructures by enabling resilient and evasive communication with Command and Control (C&C) servers. Traditional detection methods—rooted in statistical heuristics, feature engineering, and shallow machine learning—struggle to adapt to the increasing sophistication, linguistic mimicry, and adversarial variability of DGA variants. The emergence of Large Language Models (LLMs) marks a transformative shift in this landscape. Leveraging deep contextual understanding, semantic generalization, and few-shot learning capabilities, LLMs such as BERT, GPT, and T5 have shown promising results in detecting both character-based and dictionary-based DGAs, including… More >

Hafiz Muhammad Waqas¹, Tahir Mahmood^1,2, Walid Emam³, Ubaid ur Rehman⁴, Dragan Pamucar^5,*

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4699-4727, 2025, DOI:10.32604/cmc.2025.065011 - 30 July 2025

Abstract Cyber threat detection is a crucial aspect of contemporary cybersecurity due to the depth and complexity of cyberattacks. It is the identification of malicious activity, unauthorized access, and possible intrusions in networks and systems. Modern detection methods employ artificial intelligence and machine learning to study vast amounts of data, learn patterns, and anticipate potential threats. Real-time monitoring and anomaly detection improve the capacity to react to changing threats more rapidly. Cyber threat detection systems aim to reduce false positives and provide complete coverage against the broadest possible attacks. This research advocates for proactive measures and… More >

Adam Khalid¹, Anazida Zainal¹, Fuad A. Ghaleb², Bander Ali Saleh Al-rimy³, Yussuf Ahmed^2,*

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5665-5691, 2025, DOI:10.32604/cmc.2025.063451 - 30 July 2025

Abstract Advanced Persistent Threats (APTs) represent one of the most complex and dangerous categories of cyber-attacks characterised by their stealthy behaviour, long-term persistence, and ability to bypass traditional detection systems. The complexity of real-world network data poses significant challenges in detection. Machine learning models have shown promise in detecting APTs; however, their performance often suffers when trained on large datasets with redundant or irrelevant features. This study presents a novel, hybrid feature selection method designed to improve APT detection by reducing dimensionality while preserving the informative characteristics of the data. It combines Mutual Information (MI), Symmetric… More >

Akhila Reddy Yadulla, Mounica Yenugula, Vinay Kumar Kasula^*, Bhargavi Konda, Bala Yashwanth Reddy Thumma

Journal on Internet of Things, Vol.7, pp. 19-48, 2025, DOI:10.32604/jiot.2025.062733 - 11 July 2025

Abstract This study systematically reviews the Internet of Things (IoT) security research based on literature from prominent international cybersecurity conferences over the past five years, including ACM Conference on Computer and Communications Security (ACM CCS), USENIX Security, Network and Distributed System Security Symposium (NDSS), and IEEE Symposium on Security and Privacy (IEEE S&P), along with other high-impact studies. It organizes and analyzes IoT security advancements through the lenses of threats, detection methods, and defense strategies. The foundational architecture of IoT systems is first outlined, followed by categorizing major threats into eight distinct types and analyzing their More >

Ammar Odeh^*, Anas Abu Taleb

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 447-461, 2025, DOI:10.32604/cmc.2025.065426 - 09 June 2025

Abstract The increasing deployment of Internet of Things (IoT) devices has introduced significant security challenges, including identity spoofing, unauthorized access, and data integrity breaches. Traditional security mechanisms rely on centralized frameworks that suffer from single points of failure, scalability issues, and inefficiencies in real-time security enforcement. To address these limitations, this study proposes the Blockchain-Enhanced Trust and Access Control for IoT Security (BETAC-IoT) model, which integrates blockchain technology, smart contracts, federated learning, and Merkle tree-based integrity verification to enhance IoT security. The proposed model eliminates reliance on centralized authentication by employing decentralized identity management, ensuring tamper-proof… More >

Muhammad Armghan Latif¹, Zohaib Mushtaq^2,*, Saifur Rahman³, Saad Arif⁴, Salim Nasar Faraj Mursal³, Muhammad Irfan³, Haris Aziz⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.2, pp. 1667-1695, 2025, DOI:10.32604/cmes.2024.056850 - 27 January 2025

Abstract Ransomware attacks pose a significant threat to critical infrastructures, demanding robust detection mechanisms. This study introduces a hybrid model that combines vision transformer (ViT) and one-dimensional convolutional neural network (1DCNN) architectures to enhance ransomware detection capabilities. Addressing common challenges in ransomware detection, particularly dataset class imbalance, the synthetic minority oversampling technique (SMOTE) is employed to generate synthetic samples for minority class, thereby improving detection accuracy. The integration of ViT and 1DCNN through feature fusion enables the model to capture both global contextual and local sequential features, resulting in comprehensive ransomware classification. Tested on the UNSW-NB15 More >

Carlos Merlano^*

Journal of Cyber Security, Vol.6, pp. 89-116, 2024, DOI:10.32604/jcs.2024.056164 - 06 December 2024

Abstract The constantly increasing degree and frequency of cyber threats require the emergence of flexible and intelligent approaches to systems’ protection. Despite the calls for the use of artificial intelligence (AI) and machine learning (ML) in strengthening cyber security, there needs to be more literature on an integrated view of the application areas, open issues or trends in AI and ML for cyber security. Based on 90 studies, in the following literature review, the author categorizes and systematically analyzes the current research field to fill this gap. The review evidences that, in contrast to rigid rule-based… More >