Project Goal

COMPACT will propose methodologies aimed at compressing feature-based representations of network traffic in edge (e.g., IoT) and core (e.g., backbone) scenarios.

Funding

COMPACT is funded by the Italian Ministry of University and Research (MUR) under the PRIN (Progetti di Rilevante Interessse Nazionale) 2022 grant CUP D53D23001340006.

Time span

COMPACT will run for 24 months, from 09/2023 to 09/2025

Partners

COMPACT is a collaboration between Politecnico di Milano and Università degli Studi di Trieste.

Project Goals

COMPACT will propose methodologies aimed at compressing feature-based representations of network traffic (including KPIs), focusing in particular on lossy compression schemes and novel traffic representations based on machine-learning techniques. The compression techniques will be instrumental to network analysis, through the definition of specific rate-accuracy models which describe the impact of lossy coding traffic features on the accuracy of network traffic analysis tasks.

Lossy compression techniques for network traffic features

COMPACT will explore the application of lossy compression techniques to network features and KPIs, utilising a diverse set of methodologies tailored to the specific traffic representations considered.

  • Flow-based features: intra-flow coding, inter-flow coding, scalar/vector quantization, sparse coding
  • Window-based features: differential coding exploiting temporal correlation
  • Network KPIs: unique characteristics that require hybrid techniques

Compressed traffic representations

COMPACT aims to use approaches based on ML to develop generic compression and representation algorithms for network data.

In contrast to previous works, we aim to provide a comprehensive framework for representing different network entities (flows, features, endpoints, domains) based on machine learning techniques.

Rate-accuracy models for network traffic analysis

COMPACT will devise specific rate-accuracy models which describe the impact of lossy coding on the final accuracy performance of specific traffic analysis tasks. Several among the most common traffic analysis tasks will be considered, with key examples taken from both centralised/core and edge/IoT network architectures.

  • Traffic classification / intrusion detection
  • Device identification