Noura Mohammed Alaskar¹, Muzammil Hussain², Saif Jasim Almheiri¹, Atta-ur-Rahman³, Adnan Khan^4,5,6, Khan M. Adnan^7,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074041 - 10 February 2026

Abstract The increasing number of interconnected devices and the incorporation of smart technology into contemporary healthcare systems have significantly raised the attack surface of cyber threats. The early detection of threats is both necessary and complex, yet these interconnected healthcare settings generate enormous amounts of heterogeneous data. Traditional Intrusion Detection Systems (IDS), which are generally centralized and machine learning-based, often fail to address the rapidly changing nature of cyberattacks and are challenged by ethical concerns related to patient data privacy. Moreover, traditional AI-driven IDS usually face challenges in handling large-scale, heterogeneous healthcare data while ensuring data… 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 >

Adnan Hussein¹, Abdulwahab Ali Almazroi^2,*

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3853-3866, 2023, DOI:10.32604/cmc.2023.040856 - 26 December 2023

Abstract The advances in technology increase the number of internet systems usage. As a result, cybersecurity issues have become more common. Cyber threats are one of the main problems in the area of cybersecurity. However, detecting cybersecurity threats is not a trivial task and thus is the center of focus for many researchers due to its importance. This study aims to analyze Twitter data to detect cyber threats using a multiclass classification approach. The data is passed through different tasks to prepare it for the analysis. Term Frequency and Inverse Document Frequency (TFIDF) features are extracted… More >