Anomaly Detection & Real Time Analytics

In an increasingly connected world, thanks to paradigms such as the Internet of Things (IoT), Big Data and eHealth, more and more data is being generated. In this context, we find instances (anomalies) that do not follow the expected behavior or patterns. Anomaly detection is the discipline that involves identifying these rare or unusual patterns in data. It is critical in many recent applications such as healthcare, network surveillance, IoT sensor monitoring and industry.  

Main current DaSCI research lines:

  • Interpretability and explainability: One of the most prominent demands from companies is the ability to understand the model acting as an anomaly detector. This goal implies two complementary objectives: Firstly, improving model interpretability; and secondly, providing insight into the model’s functioning, making it explainable.
  • Deep learning for complex anomalies: DL is a powerful tool for anomaly detection, applied in two scenarios: static data (using autoencoders) and time series (using convolutional and recurrent neural networks). GANs have also gained popularity in anomaly detection due to their effectiveness with limited labelled data.
  • False positive mitigation: This task aims at reducing the number of false positives labelled by the anomaly detector. 
  • Big Data scenarios and model fusion in distributed environments: With the explosion of data, popularisation of sensors, and automation in data acquisition and storage, the anomaly detection problem has become a Big Data problem, where the fusion of information/models will strongly impact the quality of the final system.

Related Researchers:


  Name Email Area Cat.
Espinilla Estévez, Macarena
García Gil, Diego Jesús
García López, Salvador
Segovia Román, Fermín fsegovia@ugr.BGeV6yLUGes
Ventura Soto, Sebastián sventura@uco.RiIXgqKDE4hNes