Novel Anomaly Detection Methods for Telecommunication Data Streams.

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Anomaly detection is used in many places, and almost everywhere lots of data are processed, to answer questions like "is this transaction fraudulent?" or "do we need to switch off this expensive piece of machinery and do a maintenance check?" or "is there a planet orbiting this star?". The methods used to do this are complex and varied, and not all of them are fast enough to function well on streams of data that arrive in real time.This PhD approaches the anomaly detection problem from a statistical standpoint. Instead of heavy models that require lots of training data and computational power, it looks at lighter-touch algorithms that can work well in applications where efficiency is important and detect anomalies in real time as soon as they develop. This involves testing, evaluating, and developing methods using both real and simulated datasets.The project is sponsored by BT, and some of the problems they tackle are about flagging up issues in the telecommunications network that engineers need to go out and fix. These show up as strange blips in overall network usage over time against a backdrop of normal human behaviour (which can itself be very strange). Dealing with ways to distinguish anomalies from the varying structure in the data signal is one of the project's focus areas.In partnership with BT.