Novel Anomaly Detection Methods for Telecommunication Data Streams.

电信数据流的新颖异常检测方法。

• 批准号: 2284300
• 金额: --
• 依托单位: Lancaster University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2284300
• 关键词: Novel; Anomaly; Detection; Methods; Telecommunication

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.

异常检测在许多地方都有使用，几乎在任何地方都要处理大量数据，以回答诸如“这笔交易是否欺诈？或者“我们需要关掉这台昂贵的机器，做一次维修检查吗？““或者“有行星围绕这颗星星运行吗？".用来做这个的方法是复杂和多样的，并不是所有的方法都足够快，可以在真实的时间到达的数据流上运行良好。它不是需要大量训练数据和计算能力的繁重模型，而是着眼于更轻松的算法，这些算法可以在效率很重要的应用中很好地工作，并在异常出现时立即真实的检测异常。这涉及使用真实的和模拟数据集进行测试、评估和开发方法。该项目由英国电信赞助，他们解决的一些问题是关于标记电信网络中的问题，工程师需要出去解决。在正常人类行为的背景下，随着时间的推移，这些在整体网络使用中表现为奇怪的光点（这本身就很奇怪）。该项目的重点领域之一是如何区分数据信号中的异常结构。