Maritime anomaly detection using Gaussian Process active learning
Maritime anomaly detection using Gaussian Process active learning
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发表时间：
2012-07
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通讯作者：
K. Kowalska;Leto Peel
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作者：
K. Kowalska;Leto Peel
正常船舶行为的模型对于检测非法、可疑或不安全的行为非常有用;例如船舶盗窃、毒品走私、人口贩运或不良航行。这项工作提出了一个数据驱动的非参数贝叶斯模型，高斯过程的基础上，正常的航运行为建模。该模型从自动识别系统（AIS）数据中学习，并使用主动学习范式来选择数据的信息子样本，以降低训练的计算复杂度。由此产生的模型允许根据其当前纬度和经度的速度为每个新观察到的传输计算正态性的度量。使用这种正常性的衡量标准，船舶可以被识别为潜在的异常，并优先进行进一步调查。该模型的性能进行评估，它的能力，以检测人工生成的AIS异常在英国各地的位置。最后，从人工和真实的船舶数据的案例研究，以检测异常轨迹的模型进行了演示。
A model of normal vessel behaviours is useful for detecting illegal, suspicious, or unsafe behaviour; such as vessel theft, drugs smuggling, people trafficking or poor sailing. This work presents a data-driven non-parametric Bayesian model, based on Gaussian Processes, to model normal shipping behaviour. This model is learned from Automatic Identification System (AIS) data and uses an Active Learning paradigm to select an informative subsample of the data to reduce the computational complexity of training. The resultant model allows a measure of normality to be calculated for each newly-observed transmission according to its velocity given its current latitude and longitude. Using this measure of normality, ships can be identified as potentially anomalous and prioritised for further investigation. The model performance is assessed by its ability to detect artificially generated AIS anomalies at locations around the United Kingdom. Finally, the model is demonstrated on case studies from artificial and real vessel data to detect anomalies in unusual tracks.