Abstract Machine Learning (ML) is integrated into a growing number of systems for various
applications. Because the performance of an ML model is highly dependent on the quality of …

Unsupervised anomaly detection (AD) is critical for a wide range of practical applications,
from network security to health and medical tools. Due to the diversity of problems, no single …

As machine learning is widely applied to improve the efficiency and effectiveness of
manufacturing systems, the automated selection of appropriate algorithms and …

Since network systems have become increasingly large and complex, the limitations of
traditional abnormal packet detection have gradually emerged. The existing detection …

Detecting anomalies is crucial for a range of applications, including network security and
healthcare. A primary challenge in anomaly detection (AD) is its unsupervised nature …

Since its inception as a branch of Artificial Intelligence, Machine Learning (ML) has
flourished in recent years. A variety of approaches have been proposed based on concepts …