Machine Learning Based Automated Process for Predicting the Anomaly in AIS Data

Abstract

In this paper, we present an automated process for detecting the anomaly in Automatic Identification System (AIS) data. Machine learning approaches have been employed to automatically detect anomalies in the AIS data. The opensource AIS data is been used to evaluate the performance of the proposed approach. Supervised machine learning approaches like K Nearest Neighbor, Random Forest, Support Vector Machine classifier is employed to predict the anomalies in the AIS data. The AIS data does not contain the ground truth labels and supervised learning algorithms need labelling data, to address this issue, we employed an unsupervised approach to label the data based on the prior information and characteristics of the AIS data. The labelled data is then used to train the supervised machine learning models. The proposed approach with support vector machine classifier has classified the AIS data into normal and anomaly with an accuracy of 96.5%.