Vessel route anomaly detection with Hadoop MapReduce

We present a two-level approach to detect abnormal activities for vessels' routes. The data is obtained from the Automatic Identification System (AIS) which is required to be installed on vessels over specific gross tonnage. In the first level, we develope a Clustering algorithm: Density-based Spatial Clustering of Applications with Noise considering Speed and Direction (DBSCAN_SD). This algorithm is applied to pre-cluster the data points. Using domain knowledge in maritime, experts adjust the results produced by DBSCAN_SD with extra features. In this way, we get the optimal labeling result about whether a data point is normal or abnormal. In the second level, we use the labeled data generated in the first level to train the Parallel Meta-Learning (PML) algorithm on Hadoop. The results show that both accuracy and time complexity results are improved when we increase the number of nodes in a cluster.

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