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 …

A growing number of research papers shed light on automated machine learning (AutoML)
frameworks, which are becoming a promising solution for building complex machine …

We propose a meta-learning method to improve the anomaly detection performance on
unseen target tasks that have only unlabeled data. Existing meta-learning methods for …

Recent studies give more attention to the anomaly detection (AD) methods that can leverage
a handful of labeled anomalies along with abundant unlabeled data. These existing …

Anomaly Detection (AD) is an important research topic, with very diverse applications such
as industrial defect detection, medical diagnosis, fraud detection, intrusion detection, etc …

Manually finding the best combination of machine learning training algorithm, model and
hyperparameters can be challenging. In supervised settings, this burden has been …

Artificial Intelligence (AI)-based classifiers rely on Machine Learning (ML) algorithms to
provide functionalities that system architects are often willing to integrate into critical Cyber …

Intrusion Detection Systems (IDSs) play a vital role in securing today's Data-Centric
Networks. In a dynamic environment such as the Internet of Things (IoT), which is vulnerable …

Due to the great complexity, heterogeneity, and variety of services, anomaly detection is
becoming an increasingly important challenge in the operation of new generations of mobile …

The last decade has witnessed the explosion of machine learning research studies with the
inception of several algorithms proposed and successfully adopted in different application …