论文标题
机器学习 - 使用AIS数据在海上导航中检测到异常检测
Machine Learning-Assisted Anomaly Detection in Maritime Navigation Using AIS Data
论文作者
论文摘要
自动识别系统（AIS）报告了船只的静态和动态信息，这对于海上交通状况意识至关重要。但是，可以关闭AIS发音来隐藏可疑活动，例如非法捕鱼或盗版。因此，本文使用现实世界的AIS数据来分析海上领域中各种异常的成功检测可能性。我们提出了一个基于多级人工神经网络（ANN）的异常检测框架，以对有意和非意外的AIS开关开关异常进行分类。多类异常框架由于各种原因（例如，频道效应或有意进行非法活动）捕获了AIS消息撤离。我们从现实世界中的AIS数据中提取位置，速度，课程和定时信息，并使用它们来训练2级（正常和异常）以及3级（正常的，停电和异常）异常检测模型。我们的结果表明，这些模型的总体准确性约为99.9％，并且能够按微秒顺序对测试样本进行分类。
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.
