Novel Anomaly Detection Methods for Telecommunication Data Streams.

异常检测被用于许多地方，几乎所有地方都要处理大量数据，以回答诸如“这笔交易是否存在欺诈？”或“我们是否需要关闭这台昂贵的机器并进行维护检查？”或“是否有行星围绕这颗恒星运行？”之类的问题。用于此目的的方法复杂多样，并且并非所有方法都足够快，可以很好地处理实时到达的数据流。本博士从统计学的角度研究异常检测问题。与需要大量训练数据和计算能力的重型模型不同，它着眼于更轻的触摸算法，这些算法可以在效率很重要的应用中很好地工作，并在异常发生时立即实时检测异常。这包括使用真实和模拟数据集测试、评估和开发方法。这个项目是由英国电信赞助的，他们解决的一些问题是关于电信网络中需要工程师出去解决的问题。随着时间的推移，在人类正常行为的背景下（这本身就很奇怪），这些网络使用情况会出现奇怪的变化。处理如何从数据信号的变化结构中区分异常是该项目的重点领域之一。与英国电信合作。

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.