An Alternative Metric for Detecting Anomalous Ship Behavior Using a Variation of the DBSCAN Clustering Algorithm
This work addresses the need for quick and accurate anomaly detection in maritime security or monitoring, but it is incremental as it builds on an existing algorithm variation.
The paper tackles the problem of detecting anomalous ship behavior by applying a variation of the DBSCAN algorithm to AIS data, introducing an alternative anomaly metric that is more statistically informative than previous suggestions.
There is a growing need to quickly and accurately identify anomalous behavior in ships. This paper applies a variation of the Density Based Spatial Clustering Among Noise (DBSCAN) algorithm to identify such anomalous behavior given a ship's Automatic Identification System (AIS) data. This variation of the DBSCAN algorithm has been previously introduced in the literature, and in this study, we elucidate and explore the mathematical details of this algorithm and introduce an alternative anomaly metric which is more statistically informative than the one previously suggested.