Density peak-based clustering of industrial control protocols for reverse engineering

Abstract

Message clustering is the first and most crucial step in protocol reverse engineering, and the accuracy of protocol clustering directly affects protocol format extraction and state machine inference. The existing text protocol clustering methods do not apply to industrial control protocols, and the number of clusters and cluster centers cannot be determined automatically. The n-gram model is used to extract the protocol message features and construct the message feature vector for the clustering algorithm, which preserves the feature information in the protocol messages; density peak clustering is used to avoid the problem that the initial number of clusters and cluster centers cannot be determined automatically, and the automatic clustering of industrial control protocols is realized. It is tested on three commonly used industrial control protocol datasets, Modbus, DNP3, and S7common, and achieves good results on two metrics, purity and F1 score.