Sapienza University of Rome, May 2019
Author: Federico Bacci
Thesis Advisor: Prof. Ioannis Chatzigiannakis
The scope of this repository is to share the code, data and documentation of my Master Thesis.
More and more embedded processor are activated everyday, most of them to industrial equipment to create the fourth industrial revolution. These embedded processor sense and process data to create insights enabling smart factories that can operate without physical intervention opening the way to new possibilities and new challenges, one of all is the security of the data and of the networks of these industries. Despite the compelling features of Industrial Internet of Things, the security of such network is impeding their rapid deployment. In this thesis we try to use machine learning to analyze the Intrusion detection in such networks (IPv6 based) using data from both simulated and real world deployment of Internet of Things Networks. We propose a data-driven anomaly detection that operates at transport layer of 6LoWPAN deployments and exploring the possibilities of different tools.
- Master Thesis documentation Google Docs - Pdf
- Master Thesis presentation Google Slides - Pdf
- Source Code
- Published Research Paper Google Drive - Pdf