Time: 14:00 – 16:00, Nov. 23, 2020
On Teams: please drop an email to seccis AT cnam DOT fr to join.

5 new PhD students joined the ROC team in fall 2020 and will deliver quick talks to present their Ph.D. thesis subject.


Alessandro Aimi
co-advised by Amina Boubendir (Orange), Stéphane Rovedakis (Cnam), Stefano Secci (Cnam), Fabrice Guillemin (Orange)

Biographie : Alessandro Aimi graduated in Computer Science and Engineering at Politecnico di Milano (Italy) in July 2020, following a study track focused on Artificial Intelligence and algorithm design. He joined Cnam in October 2020 to work on a Ph.D. thesis on virtualized network automation algorithms and platforms in collaboration with Orange Labs.

Title : Automation in network management for Edge and Beyond-5G infrastructures

Abstract : Novel technologies in the area of network virtualization and softwarization, accelerated by edge computing and the use of artificial Intelligence, open new ways of designing network architectures that bring us towards beyond-5G and 6G networks, with massive distribution and automation of network elements and processes. This will make it possible to achieve the changes and demands identified in many new social and industrial use cases. The goal of my Ph.D. thesis is to conceive and evaluate the algorithmic core needed to run network automation platforms and solve data-driven decision-making problems therein, in full integration with network and service life-cycle management envisioned for future networks. Particular attention will be devoted to elaborate scalable ways to run learning and classification algorithms in a distributed way, in order to cope with novel infrastructures including network slicing and IoT access network environments.


Kiranpreet Kaur
co-advised by Veronica Quintuna Rodriguez (Orange), Françoise Sailhan (Cnam), Stefano Secci (Cnam), Fabrice Guillemin (Orange)

Biographie : Kiranpreet Kaur earned a master degree from the University of Rennes 1, majoring in Cloud computing & services. Further, she joined  SAP Labs (France) in 2020 and Orange labs Rennes in 2019 for research internship. She is now PhD candidate at Cnam and Orange Labs. 

Title : Migration and dynamic instantiation of microservices  while maintaining end-to-end service chain performance.

Abstract: The key perspective of the thesis is to study the ability to perform modifications in a set of microservices hosted in containers and structured in groups (slices) without affecting the running services and the performance of the overall network functions (dependent themselves of the requirements of the slices and their service-level agreement).


Yacine Anser 
co-advised by Christel Gaber (Orange), Samia Bouzefrane (Cnam), Yacoub Meziane (Cnam), Jean-Luc Grimault (Orange), Pierre Paradins (Cnam)

Biographie : Yacine Anser est diplômé de l’INSA de Toulouse en informatique et réseaux, spécialité sécurité et sûreté de l’information, orientation Réseaux et Télécom. Il est titulaire du certificat TLSSEC (Toulouse sécurité) de l’INSA Toulouse, ENSEEIHT et ENAC. Il est inscrit en thèse au Cnam et à Orange Labs Caen.

Title : Gestion de service de sécurité centrée sur la confiance pour la bordure de réseau

Abstract: Avec l’arrivée des réseaux programmables et des fonctions de réseaux virtualisées, les opérateurs réseaux peuvent offrir des primitives de sécurité allant au-delà de la sécurisation des communications. Plus précisément, ils peuvent enrichir des objets contraintes (ne pouvant pas assurer par eux-mêmes leurs besoins de s”curit”) de fonctions de sécurité. Ces fonctions pourraient être fournies par l’opérateur ou par des tiers. Dans ce contexte, l’opérateur doit pouvoir évaluer les fonctions et les équipements assurant cette sécurisation. Avec la multiplication des objets connectés, de nouveaux modèles doivent être conçus pour mettre en oeuvre les moyens de sécurité cités précédemment. La notion de confiance devient centrale pour l’orchestration de ces services de sécurité. L’objectif principal de la these consistera a définir des modeles et outils permettant a un gestionnaire de services de sécurité de fournir et garantir un niveau de qualité de service basé sur la confiance que l’opérateur de l’infrastructure accorde aux composants du système ainsi qu’à leurs fournisseurs. T


Yulliwas Ameur 
co-advised by Samia Bouzefrane (Cnam), Vincent Audiger (Cnam)

Biographie : Yulliwas Ameur  received his Master degree in ‘Mathematics of Cryptography and Communications’ from the University of Paris 8 in 2019, after a master internship on the topic of resilience against side-channel attacks on code-based cryptographic schemes, at Inria Rennes – Bretagne Atlantique. He was granted the SMI doctoral school scholarship from Cnam in 2020.

Title: Privacy-Preserving Deep Learning via Homomorphic Encryption

Abstract: A large amount of data is increasingly collected from different IoT devices such as health devices, automotive equipment, smart home, etc. To avoid processing data within the IoT devices, the trend is to outsource the sensed data to the Cloud that has both resourceful data storage and data processing, and to analyse them using powerful tools such as machine learning and statistical techniques. Nevertheless, the externalized data may be sensitive, and the users may lose privacy on the data content while allowing the cloud providers to access and possibly use these data to their own business. To avoid this situation and preserve data privacy in the Cloud datacenter, one possible solution is to use the fully homomorphic encryption (FHE) that assures both confidentiality and efficiency of the processing. Hence, the big issue is to how to apply FHE on ML techniques. This talk will focus on the limitations of HE when it’s combined with Deep Learning algorithms.


Christophe Maudoux 
co-advised by Selma Boumerdassi (Cnam), Stefano Secci (Cnam)

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.

Title: Machine learning based real-time anomalies detection of botnets in edge networks

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. 



Lightening presentations by new ROCkers PhD students – Nov. 23, 2020
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