Anomaly detection has numerous applications in diverse fields. For example, it has been
widely used for discovering network intrusions and malicious events. It has also been used …

We present an overview of the major developments in the area of detection of outliers.
These include projection pursuit approaches as well as Mahalanobis distance‐based …

Many machine learning algorithms and almost all deep learning architectures are incapable
of processing plain texts in their raw form. This means that their input to the algorithms must …

Over the last three decades, Network Intrusion Detection Systems (NIDSs), particularly,
Anomaly Detection Systems (ADSs), have become more significant in detecting novel …

For statistical learning, categorical variables in a table are usually considered as discrete
entities and encoded separately to feature vectors, eg, with one-hot encoding.“Dirty” non …

A close relationship exists between the advancement of face recognition algorithms and the
availability of face databases varying factors that affect facial appearance in a controlled …

Abnormal network traffic analysis has become an increasingly important research topic to
protect computing infrastructures from intruders. Yet, it is challenging to accurately discover …

In massive data analysis, data usually come in streams. In the last years, several studies
have investigated novelty detection in these data streams. Different approaches have been …

Over the past few years, Internet of Things security has attracted the attention of many
researchers due to its challenging and constrained nature. Particularly in the development of …

Real-world applications usually encounter data with various modalities, each containing
valuable information. To enhance these applications, it is essential to effectively analyze all …