Name: BRUNO MISSI XAVIER
Publication date: 21/06/2024
Examining board:
Name |
Role |
|---|---|
| ALBERT CABELLOS APARICIO | Examinador Externo |
| ANDRE GEORGHTON CARDOSO PACHECO | Examinador Interno |
| MAGNOS MARTINELLO | Presidente |
| MARCO RUFFINI | Coorientador |
| MARIAM KIRIAN | Examinador Externo |
Pages
Summary: Traffic management plays a crucial role for this expansive global connectivity. In this
context, traffic classification strategically differentiates a range of applications and its
requirements. This transformation enhances network agility and facilitates the direct
integration of Machine Learning (ML) into the network infrastructure, fundamentally
changing traffic management by promoting proactive data processing and analysis within
the network. The synergy of Network Softwarization (NS) with ML not only leads to
reduced latency and improved load management, but also increases the capacity to
effectively handle larger volumes of data. Consequently, networks are better equipped to
meet the complex demands of modern digital ecosystems, ensuring robust and efficient
connectivity.
This thesis delved into the domain of programmable networks, with a specific focus on
implementing ML for traffic classification within the network architecture components,
including the Radio Access Network (RAN) and programmable data planes. This approach
represents a significant departure from the traditional traffic classification techniques,
which are typically deployed on end-hosts. It also paves the way for integrating Cross-
Domain Artificial Intelligence (AI) capabilities within the network, facilitated by multi-view
learning. More specifically, we advanced the state-of-the-art with four main contributions:
(i) We design a framework named Early Attack Guarding and Learning Engine (EAGLE)
as the first defense line against a set of cyber threats. Our framework explores Open
Radio Access Network (O-RAN) to collect measurements from the air interface (that
is, Physical and Medium Access Control) to identify and early mitigate malicious flows;
(ii) We introduce MAP4 that demonstrates the feasibility of deploying ML models (that
is, decision trees and Random Forest) in the data plane. To achieve this, we rely on
the P4 language to deploy a pre-trained model to accurately classify flows at line rate;
(iii) We proposed an In-Network Concept Drift to deal with the dynamic nature of the
network traffic. This approach detects changes in traffic distribution by implementing
Exponentially Weighted Moving Average (EWMA) overcoming the P4 limitations; (iv)
Our Cross-Domain AI integrates multiple layers (RAN and programmable data planes) to
form an in-network stacking of ML classifiers under the multi-view learning perspective.
This innovative approach overcomes the challenges of a single layer in order to improve
the overall accuracy of the classification system.
