Publications by authors named "Sultan H Almotiri"
The introduction of quantum computing has transformed the setting of information technology, bringing both unprecedented opportunities and significant challenges. As quantum technologies continue to evolve, addressing their implications for software security has become an essential area of research. This paradigm change provides an unprecedented chance to strengthen software security from the start, presenting a plethora of novel alternatives.
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- The explosive growth of the internet and digital platforms has led to increasing challenges in data transmission security, highlighting the need for robust network security measures to protect sensitive information.
- This research uses a Multi-Criteria Decision Making (MCDM) approach, focusing on a fuzzy TOPSIS method, to evaluate and enhance resilience against DDoS attacks by assessing various security attributes and vulnerabilities.
- The findings reveal that network N6 is the most secure against DDoS attacks, providing strategic recommendations for organizations to strengthen cybersecurity and better protect their operations and sensitive data from evolving cyber threats.
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- Rapid advancements in deepfake technology increase the risks of misinformation and fraud through manipulated media, highlighting a lack of comprehensive deepfake detection techniques.
- This research systematically surveys current digital forensic methods for detecting deepfakes, analyzing recent publications, datasets, and the effectiveness of various detection approaches for different media types.
- The study emphasizes the need for ongoing innovation in detection methods and addresses the limitations of current research, aiming to provide valuable insights for researchers, practitioners, and policymakers.
The significant growth in the use of the Internet and the rapid development of network technologies are associated with an increased risk of network attacks. Network attacks refer to all types of unauthorized access to a network including any attempts to damage and disrupt the network, often leading to serious consequences. Network attack detection is an active area of research in the community of cybersecurity.
View Article and Find Full Text PDFThe purpose of this research was to provide a "systematic literature review" of knee bone reports that are obtained by MRI, CT scans, and X-rays by using deep learning and machine learning techniques by comparing different approaches-to perform a comprehensive study on the deep learning and machine learning methodologies to diagnose knee bone diseases by detecting symptoms from X-ray, CT scan, and MRI images. This study will help those researchers who want to conduct research in the knee bone field. A comparative systematic literature review was conducted for the accomplishment of our work.
View Article and Find Full Text PDFDeep Learning (DL) algorithms enabled computational models consist of multiple processing layers that represent data with multiple levels of abstraction. In recent years, usage of deep learning is rapidly proliferating in almost every domain, especially in medical image processing, medical image analysis, and bioinformatics. Consequently, deep learning has dramatically changed and improved the means of recognition, prediction, and diagnosis effectively in numerous areas of healthcare such as pathology, brain tumor, lung cancer, abdomen, cardiac, and retina.
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