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Mubarak Albathan

As seen in: MDPI
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Articles

Enhancing Cloud-Based Security: A Novel Approach for Efficient Cyber-Threat Detection Using GSCSO-IHNN Model

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By Divya Ramachandran, Mubarak Albathan, Ayyaz Hussain, Qaisar Abbas
| MDPI
6. Discussions The initial step in this research involves collecting intrusion benchmark datasets such as CICIDS 2017, BoT-IoT, and NSL-KDD. These datasets often contain incomplete or missing information, which can impact the performance of classification and detection. Preprocessing and normalization operations are critical to address these issues and ensure the quality of the data used for training and testing machine learning models.
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Deep-Ocular: Improved Transfer Learning Architecture Using Self-Attention and Dense Layers for Recognition of Ocular Diseases

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By Qaisar Abbas, Mubarak Albathan, Abdullah AlTameem, Riyad Saleh Almakki
| MDPI
4. Experimental Results 4.1. Experimental Setup All deep learning (DL) models involved in the study were pre-trained on ImageNet, and their training and testing were conducted using the publicly available datasets. This framework was compiled with CUDA 8.0 and CUDNN 5.1 to enhance performance and efficiency. The experiments were executed on a high-powered workstation with three Intel CPUs, and a substantial 64 GB of memory.
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ROAST-IoT: A Novel Range-Optimized Attention Convolutional Scattered Technique for Intrusion Detection in IoT Networks

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By Ganeshkumar Perumal, Gopalakrishnan Subburayalu, Mubarak Albathan, Anandaraj Mahalingam
| MDPI
This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
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