Đang chuẩn bị liên kết để tải về tài liệu:
Robust anomaly detection methods for contamination network data

Không đóng trình duyệt đến khi xuất hiện nút TẢI XUỐNG Tải xuống

Recently, latent representation models, such as Shrink Autoencoder (SAE), have been demonstrated as robust feature representations for one-class learning-based network anomaly detection. In these studies, benchmark network datasets that are processed in laboratory environments to make them completely clean are often employed for constructing and evaluating such models. |