Time: 14:00 – 16:00, Nov. 23, 2020
On Teams: please drop an email to seccis AT cnam DOT fr to join.
3 new PhD students will deliver quick talks to present their Ph.D. thesis subject.
co-advised by Jean-Michel Douin and Samia Bouzefrane and Pierre Paradinas (Cnam)
Title: Intelligent self-adaptation of reputation-driven industrial IoT systems
Abstract: The number of IoT devices in Industrial networks is steadily rising. All of these devices are not created equal, and fulfilling the requirements of the network or the users requires adaptability and flexibility. One such requirement is the safety of the data flowing through a system. Some devices are inherently safer and more trustworthy than others, but additional factors may be at play. Our goal in this PhD is to study the trustworthiness of devices in a system to provide the system the ability to adapt in the most efficient way, through the deployment of software components.
Biographie : Antoine Crestani graduated from IMT Mines Albi in IT Systems Engineering in July 2020, after finishing a masters internship for Total in Denmark, where he built an internal file management system. He joined the Cnam in February 2020.
co-advised by Selma Boumerdassi and Stefano Secci (Cnam)
Title: Machine learning based real-time anomalies detection of botnets in edge networks
Biographie : Christophe Maudoux graduated from Cnam network and system engineering track in 2019, where he is also now a part-time professor. He works on WebSSO engineering also known as identity and access management (IAM), and is part of the open source webSSO project LemonLDAP::NG core team as maintainer and advanced Perl programmer.
Abstract: The last few years have seen the emergence of communicating objects in everyday life. Primarily intended to make our lives easier, thousands of connected objects take also part in BotNets, wherein BotMasters use them to launch massive attacks. Therefore, it is necessary to be able to detect BotNets deployment steps, their command and control or attack channels. The PhD project aims to define and implement real-time machine learning algorithm to detect security anomalies and prevent attacks.
Laiza de Lara
co-advised by Stéphane Rovedakis and Stefano Secci (Cnam)
Title: Fault-tolerance analysis of distributed 5G resource allocation algorithms
Abstract: A recent work at the state of the art from Fossati et al proposes a set of algorithms for handing 5G resource allocation spanning multiple resource domaines in a distributed way instead than in a centralized way. We will present how the proposed algorithms perform under resource controller failure, comparing their performance, and proposing ways to extend them to handle controller failures.
Biographie : Laiza de Lara a obtenu un diplôme d’ingénieure électronique de l’universite tecnologique federale de Parana, Brésil, en 2021, après une année d’échange à l’ISEP, France. Elle a rejoint le Cnam en septembre 2020 pour un stage de recherche. Le sujet du stage est sur l’analyse de robustesse d’algorithmes de allocation de ressources en considérant un fournisseur de services défaillant