| KEYWORD |
Reverse engineering of industrial plants through network traffic analysis.
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 Objective:
The objective of this master thesis is to use network traffic analysis to perform reverse engineering of industrial plants, in order to identify system vulnerabilities and potential security threats. The thesis will focus on techniques for analyzing network traffic to extract information about communication protocols and interactions between plant components.
Methodology:
The thesis will be divided into three main phases:
Analysis of network traffic to identify the communication protocols used in the industrial plant.
Reverse engineering of plant components through network traffic analysis and the creation of a model of the system.
Identification of system vulnerabilities and potential security threats through analysis of the model created.
Advanced analysis tools and techniques will be used to extract information about communication protocols, data flows, and interactions between plant components. The model created through reverse engineering will allow the identification of system vulnerabilities and potential 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