GPU Accelerated Network Anomaly Detection | Unsupervised Machine Learning | Cybersecurity Project скачать в хорошем качестве

GPU Accelerated Network Anomaly Detection | Unsupervised Machine Learning | Cybersecurity Project

In this project, I implement a GPU-accelerated unsupervised network anomaly detection system designed to identify malicious and abnormal traffic patterns efficiently. The model is trained on the UGR’16 dataset and evaluated on the CDS IDS dataset, demonstrating strong generalization and performance improvements through GPU parallelization. This project focuses on: High-speed anomaly detection using GPU computing Unsupervised learning for network security Large-scale dataset processing and performance evaluation gpu accelerated machine learning network anomaly detection unsupervised machine learning cybersecurity project intrusion detection system gpu computing network security machine learning project masters project cybersecurity UGR16 dataset CDS IDS dataset anomaly detection machine learning gpu vs cpu machine learning deep learning cybersecurity