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A Machine Learning Approach for Prediction of Signaling SIP Dialogs
Publication . Pereira, Diogo; Oliveira, Rodolfo; Kim, Hyong S.; DEE - Departamento de Engenharia Electrotécnica e de Computadores; Institute of Electrical and Electronics Engineers (IEEE)
In this paper, we propose a machine learning methodology for prediction of signaling sessions established with the Session Initiation Protocol (SIP). Given the increasing importance of predicting and detecting abnormal sequences of SIP messages to avoid SIP signaling-based attacks, we first propose a Bayesian inference method capable of representing the statistical relation between a SIP message, observed by a SIP user agent or a SIP server, and prior trustworthy SIP dialogs. The Bayesian inference method, a Hidden Markov Model (HMM) enriched with $n-$ gram Markov observations, is updated over time, so the inference can be used in real-time. The HMM is then used for predicting and detecting SIP dialogs through a lightweight implementation of Viterbi algorithm for sparse state spaces. Experimental results are also reported, where a SIP dataset representing prior information collected by a SIP user agent and/or a SIP server is used to predict or detect if a received sequence of SIP messages is legitimate according to similar SIP dialogs already observed. Finally, we discuss the results obtained for a dataset of abnormal SIP sequences, not observed during the inference stage, showing the effective utility of the proposed methodology to detect abnormal SIP sequences in a short period of time.
An Adaptive Learning-Based Approach for Vehicle Mobility Prediction
Publication . Irio, Luis; Ip, Andre; Oliveira, Rodolfo; Luis, Miguel; DEE - Departamento de Engenharia Electrotécnica e de Computadores; Institute of Electrical and Electronics Engineers (IEEE)
This work presents an innovative methodology to predict the future trajectories of vehicles when its current and previous locations are known. We propose an algorithm to adapt the vehicles trajectories' data based on consecutive GPS locations and to construct a statistical inference module that can be used online for mobility prediction. The inference module is based on a hidden Markov model (HMM), where each trajectory is modeled as a subset of consecutive locations. The prediction stage uses the statistical information inferred so far and is based on the Viterbi algorithm, which identifies the subset of consecutive locations (hidden information) with the maximum likelihood when a prior subset of locations are known (observations). By analyzing the disadvantages of using the Viterbi algorithm (TDVIT) when the number of hidden states increases, we propose an enhanced algorithm (OPTVIT), which decreases the prediction computation time. Offline analysis of vehicle mobility is conducted through the evaluation of a dataset containing real traces of 442 taxis running in the city of Porto, Portugal, during a full year. Experimental results obtained with the dataset show that the prediction process is improved when more information about prior vehicle mobility is available. Moreover, the computation time of the prediction process is significantly improved when OPTVIT is adopted and approximately 90% of prediction performance can be achieved, showing the effectiveness of the proposed method for vehicle trajectory prediction.
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Fundação para a Ciência e a Tecnologia
Programa de financiamento
9471 - RIDTI
Número da atribuição
151901