PPGI PhD thesis defese (Friday 21st at 9am )

Dear all,

We cordially invite you to attend the PhD thesis defense of Bruno Missi Xavier, titled "Crossing Domains for Accuracy: In-Network Stacking of Machine Learning Classifiers."

Date: Friday, 21st
Time: 9:00 AM

The defense will take place in a hybrid format at room 34 of CT13 and also virtually at https://meet.google.com/pga-nimx-nvm.

Abstract: This research marks a significant shift from traditional traffic classification techniques, which are typically end-host based, by embedding ML directly into the network.

Key contributions of the thesis include:

MAP4: Demonstrates the deployment of ML models (such as Decision Trees and Random Forests) within the data plane using the P4 language, enabling accurate flow classification at line rate.

In-Network Concept Drift: Introduces a method to detect changes in network traffic distribution by implementing Exponentially Weighted Moving Average (EWMA) to address the limitations of P4.

Eagle Framework: Designed as a defense mechanism against cyber threats by utilizing Open Radio Access Network (O-RAN) to collect measurements from the air interface (PHY and MAC layers) for early detection and mitigation of malicious flows.

Cross-Domain AI: Integrates multiple layers (RAN and programmable data planes) to create an in-network stacking of ML classifiers under a multi-view learning approach, thereby enhancing the overall accuracy of traffic classification systems.

Members of the jury:

Prof. Magnos Martinello - UFES - supervisor

Prof. Marco Ruffini - Trinity College Dublin (TCD) - co-supervisor

Prof. Mariam Kirian - Oak Ridge National Laboratory - USA

Prof. Albert Cabellos Aparicio - UPC Barcelona Tech

Prof. Rafael Pasquini - UFU

Prof. André Pacheco - UFES


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