| KEYWORD |
Advanced network analysis techniques and self-learning for recognition of network traffic anomalies.
Thesis in external company
keywords MACHINE LEARNING, ARTIFICIAL NEURAL NETWORKS
Reference persons ALESSANDRO ALIBERTI, EDOARDO PATTI
External reference persons Alessio Viticchié
Research Groups DAUIN - GR-06 - ELECTRONIC DESIGN AUTOMATION - EDA, EDA Group, ELECTRONIC DESIGN AUTOMATION - EDA, Energy Center Lab, GR-06 - ELECTRONIC DESIGN AUTOMATION - EDA, ICT4SS - ICT FOR SMART SOCIETIES
Thesis type APPLIED RESEARCH
Description In the context of a company project (DIANA), the student will investigate the state of the art about Machine Learning techniques for network traffic analysis for automatic modelling
The thesis will be divided into two main phases:
Analysis of industrial network traffic to define reference models for normal traffic and anomalies.
Implementation of machine learning algorithms capable of recognizing the anomalies detected in network traffic and reporting any security threats
Expected results:
The thesis aims to develop an effective methodology for analyzing industrial systems through network traffic analysis. The model created through reverse engineering will allow the identification of system vulnerabilities and potential security threats, enabling security operators to take preventive measures to protect the industrial plant. Additionally, the thesis will contribute to the understanding of communication protocols used in industrial plants, allowing for the development of more effective and targeted security solutions.
Deadline 13/03/2024
PROPONI LA TUA CANDIDATURA