Chaire Cyber CNI

Chaire Cyber CNI – Cybersecurity for Critical Networked Infrastructures

Wed, Jan 25, 2021, 5pm CET I Carol Fung (Virginia Commonwealth University, US) – Security and Privacy Protection for IoT Networks

Share and like: On Jan 25, 2021, 5pm CET I Carol Fung (Virginia Commonwealth University, US), will talk about “Security and Privacy Protection for IoT Networks“. Watch the trailer here.

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Chaire CyberCNI @EDF Paris (21.9.2021) – Hassan CHAITOU, Security risk optimization for learning on heterogeneous quality data

On Sep 21, 2021, we had the pleasure to visit our partner EDF in Paris Palaiseau! Here is another highlight presentation:

Hassan CHAITOU, Security risk optimization for learning on heterogeneous quality data
A classifier is a component used in the automation of “decision-making” or complex data abstraction: intruder detection, speed limitation extraction. For an efficient classifier, the training must be on a large volume of data and be renewed over time by integrating or revoking certain learning data. From a security point of view, this process represents a risk since it offers the attacker various ways of degrading classifier performance (either by forcing classifications mischievous, either by randomly degrading its performance). These two types of attacks require more or less effort from the attacker.

This risk is exacerbated when data comes from sources (network equipment, organizations) corresponding to heterogeneous trust levels. Hassan’s thesis aims at controlling the risk associated with this update via game theory in the case where the confidence in the learning data is not homogeneous.

Chaire CyberCNI @EDF Paris (21.9.2021) – Léo LAVAUR, Federated learning for defending Cyber-Attacks

On Sep 21, 2021, we had the pleasure to visit our partner EDF in Paris Palaiseau! Here is another highlight presentation:

Léo LAVAUR, Federated learning for defending Cyber-Attacks
In 2016, Google introduced the concept of Federated Learning (FL), enabling collaborative Machine Learning (ML). FL does not share local data but ML models, offering applications in diverse domains, including cybersecurity. Léo explains how FL has been studied to overcome challenges of collaborative intrusion detection and mitigation systems. His current research focuses on applying these concepts to specific use cases, such as smart factories, autonomous vehicles, or smart healthcare.

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