基于AIS数据的机器学习辅助航海异常检测
Machine Learning-Assisted Anomaly Detection in Maritime Navigation Using AIS Data
摘要：
船舶自动识别系统（AIS）报告船舶的静态和动态信息，这对于海上交通态势感知至关重要。然而，AIS应答器可以关闭以隐藏可疑活动，如非法捕鱼或海盗行为。因此，本文使用真实世界的AIS数据来分析在海洋领域中成功检测各种异常的可能性。我们提出了一个多类人工神经网络（ANN）为基础的异常检测框架分类故意和非故意AIS开关切换异常。多类别异常框架捕获由于各种原因导致的AIS消息丢失，渠道效应或故意进行非法活动。我们从真实世界的AIS数据中提取位置、速度、航向和时间信息，并使用它们训练2类（正常和异常）和3类（正常、断电和异常）异常检测模型。我们的研究结果表明，该模型实现了约99.9%的整体准确度，并能够在微秒量级的测试样本进行分类。
英文摘要：
The automatic identification system (AIS) reports vessels' static and dynamic information, which are essential for maritime traffic situation awareness. However, AIS transponders can be switched off to hide suspicious activities, such as illegal fishing, or piracy. Therefore, this paper uses real world AIS data to analyze the possibility of successful detection of various anomalies in the maritime domain. We propose a multi-class artificial neural network (ANN)-based anomaly detection framework to classify intentional and non-intentional AIS on-off switching anomalies. The multi-class anomaly framework captures AIS message dropouts due to various reasons, e.g., channel effects or intentional one for carrying illegal activities. We extract position, speed, course and timing information from real world AIS data, and use them to train a 2-class (normal and anomaly) and a 3-class (normal, power outage and anomaly) anomaly detection models. Our results show that the models achieve around 99.9% overall accuracy, and are able to classify a test sample in the order of microseconds.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
