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 …

Over the past decade, we have witnessed an enormous amount of research effort dedicated
to the design of efficient outlier detection techniques while taking into consideration …

Novelty detection is the task of classifying test data that differ in some respect from the data
that are available during training. This may be seen as “one-class classification”, in which a …

The automatic identification system (AIS) tracks vessel movement by means of electronic
exchange of navigation data between vessels, with onboard transceiver, terrestrial, and/or …

Many anomaly detection methods exist that perform well on low-dimensional problems
however there is a notable lack of effective methods for high-dimensional spaces, such as …

Anomaly detection is an important problem that has been researched within diverse
research areas and application domains. Many anomaly detection techniques have been …

Accurate traffic classification is of fundamental importance to numerous other network
activities, from security monitoring to accounting, and from Quality of Service to providing …

Outliers once upon a time regarded as noisy data in statistics, has turned out to be an
important problem which is being researched in diverse fields of research and application …

When anomaly detection software is used as a data analysis tool, finding the hardest-to-
detect anomalies is not the most critical task. Rather, it is often more important to make sure …

In this paper, we introduce a sequential autoencoder framework using long short term
memory (LSTM) neural network for computer network intrusion detection. We exploit the …