Anomaly Detection on MIL-STD-1553 Dataset using Machine Learning Algorithms

This paper evaluates the ability of several machine learning algorithms to detect attacks that emulate normal nonperiodical messages in the MIL-STD-1553 communication traffic. The dataset for this research is highly imbalanced and most algorithms fail or simply produce poor results when classifying the data. We conduct several experiments with different machine learning algorithms to correctly classify MIL-STD-1553 dataset. We identify appropriate metrics to judge the performance of the models applied to this dataset. Using these metrics, we compare the performance of different machine learning models that we generated.