Taher Alzahrani^*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4575-4606, 2025, DOI:10.32604/cmc.2025.063448 - 19 May 2025

Abstract The rapid evolution of malware presents a critical cybersecurity challenge, rendering traditional signature-based detection methods ineffective against novel variants. This growing threat affects individuals, organizations, and governments, highlighting the urgent need for robust malware detection mechanisms. Conventional machine learning-based approaches rely on static and dynamic malware analysis and often struggle to detect previously unseen threats due to their dependency on predefined signatures. Although machine learning algorithms (MLAs) offer promising detection capabilities, their reliance on extensive feature engineering limits real-time applicability. Deep learning techniques mitigate this issue by automating feature extraction but may introduce computational overhead,… More >

Amal H. Alharbi¹, S. Karthick², K. Venkatachalam³, Mohamed Abouhawwash^4,5, Doaa Sami Khafaga^1,*

Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 2773-2787, 2023, DOI:10.32604/iasc.2023.030763 - 17 August 2022

Abstract Recent security applications in mobile technologies and computer systems use face recognition for high-end security. Despite numerous security techniques, face recognition is considered a high-security control. Developers fuse and carry out face identification as an access authority into these applications. Still, face identification authentication is sensitive to attacks with a 2-D photo image or captured video to access the system as an authorized user. In the existing spoofing detection algorithm, there was some loss in the recreation of images. This research proposes an unobtrusive technique to detect face spoofing attacks that apply a single frame… More >