Anomaly detection has been used for decades to identify and extract anomalous
components from data. Many techniques have been used to detect anomalies. One of the …

Deep learning approaches to anomaly detection (AD) have recently improved the state of
the art in detection performance on complex data sets, such as large collections of images or …

Detecting anomalous subsequences in time series data is an important task in areas
ranging from manufacturing processes over finance applications to health care monitoring …

Given the continually rising frequency of cyberattacks, the adoption of artificial intelligence
methods, particularly Machine Learning (ML), Deep Learning (DL), and Reinforcement …

Recent advances in technology have brought major breakthroughs in data collection,
enabling a large amount of data to be gathered over time and thus generating time series …

The implementation of electronics in modern vehicles has resulted in an increase in attacks
targeting in-vehicle networks; thus, attack detection models have caught the attention of the …

Anomaly detection is concerned with identifying data patterns that deviate remarkably from
the expected behavior. This is an important research problem, due to its broad set of …

Threats that have been primarily targeting nation states and their associated entities have
expanded the target zone to include the private and corporate sectors. This class of threats …

As a new term in the financial industry, FinTech has become a popular term that describes
novel technologies adopted by the financial service institutions. This term covers a large …

Multivariate time series data come as a collection of time series describing different aspects
of a certain temporal phenomenon. Anomaly detection in this type of data constitutes a …