A Comparative Study of Sequence Identification Algorithms in IoT Context

Pierre-Samuel Greau-Hamard (1) , Moïse Djoko-Kouam (1) , Yves Louët (2)

1 ECAM - ECAM Rennes - Louis de Broglie
2 IETR - Institut d'Électronique et des Technologies du numéRique

In the fast developing world of telecommunications, it may prove useful to be able to analyse any protocol one comes across, even if it is unknown. To that end, one needs to get the state machine and the frame format of the protocol. These can be extracted from network and/or execution traces via Protocol Reverse Engineering (PRE). In this paper, we aim to evaluate and compare the performance of three algorithms used as part of three different PRE systems of the literature: Aho-Corasick (AC), Variance of the Distribution of Variances (VDV), and Latent Dirichlet Allocation (LDA). In order to do so, we suggest a new meaningful metric complementary to precision and recall: the fields detection ratio. We implemented and simulated these algorithms in an Internet of Things (IoT) context, and more precisely on ZigBee Data Link Layer frames. The results obtained clearly show that the LDA algorithm outperforms AC and VDV.

Mots clés: Protocol reverse engineering; AC; VDV; LDA; ZigBee; IoT; Data link layer; Performance comparison; Protocol Reverse Engineering; Zigbee; Data Link Layer; Performance Comparison

Domaines: Réseaux et télécommunications [cs.NI]; Machine Learning [stat.ML]

Pierre-Samuel Greau-Hamard, Moïse Djoko-Kouam, Yves Louët. A Comparative Study of Sequence Identification Algorithms in IoT Context. 2nd International Conference on Advances in Signal Processing and Artificial Intelligence (ASPAI' 2020), Apr 2020, Berlin, Germany. pp.137-143. ⟨hal-03514867⟩