Anomaly Detection within High Volume Data Streams Using Deep Learning Industrial Partner: Cosmonio Ltd (

Lead Research Organisation: Durham University
Department Name: Computer Science


Deep Learning is a fast-growing area in the field of machine learning within which systems can be trained to identify patterns in a way that loosely represent biological neural computation. Recent advances in such Deep Neural Network (DNN) technology have led to a significant performance leap in pattern-recognition tasks such as computer vision (image understanding), text mining (language
understanding) and voice recognition (audio understanding). This advancement has been further enhanced by the evolution of low-cost Graphical Processing Units (GPUs) into massively-parallel computing platforms capable of processing the vast dataset requirements for such tasks.

This project looks beyond the initial applications of such technology, largely aimed at the classification of images, words and sound to a set of expected semantic labels to the problem of anomaly detection.
Asking the novel research question - what is different from normal here within this stream of data? - rather than specifically identifying the {pixel | text | word ...} pattern for {person | vehicle | dog ...} etc in the conventional sense.


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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/N509462/1 30/09/2016 29/09/2021
1966980 Studentship EP/N509462/1 01/12/2017 30/05/2021 Philip Adey