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Classification of Discrete Sequential Protocol Messages Based on LSTM Network and Transfer Learning

Discrete Sequential Protocol Messages (DSM) is the main data component in some communication networks such as Internet of Things, which is usually short, length-variable and unlabeled. This paper proposes a method based on long short-term memory (LSTM) for supervised classification of encrypted DSM data. And for unsupervised classification, the LSTM-TL method is utilized by transferring the pre-trained LSTM model on source domain to the target domain. Experiments were carried out on Text, ACARS, HTTP & SSH and AIS datasets. For supervised classification, F-Score and accuracy both reached 0.96. For unsupervised classification, FScore and accuracy were both greater than 0.96 on the first three datasets, while only 50% and 67% respectively on AIS dataset. The reason was analyzed by using dynamic time warping. The results also show that the data length can be processed is shorten to 10 bytes by processing DSM as one-dimensional time series while the traditional methods often require much more bytes.