Table of contents

Description

  • Speaker

    Hugo Lefeuvre - The University of British Columbia

Embedded systems do not benefit from strong memory protection, because they are designed to minimize cost. At the same time, there is increasing pressure to connect embedded devices to the internet, where their vulnerable nature makes them routinely subject to compromise. This fundamental tension leads to the current status-quo where exploitable devices put individuals and critical infrastructure at risk.

We present the design of a dependable embedded OS where compartmentalization and memory safety are first-class citizens. We co-design the OS with an embedded hardware platform that implements CHERI capabilities at a similar cost profile to existing chips with minimal security. We demonstrate key design benefits: fine-grained fault-tolerant compartments, OS-level support for compartment-interface hardening, and auditing facilities to thwart supply-chain attacks, among others, and show that they come at a memory usage and performance cost that allows their widespread deployment in cheap, resource-constrained devices.

Practical infos

Next sessions

  • Towards privacy-preserving and fairness-aware federated learning framework

    • September 19, 2025 (11:00 - 12:00)

    • Inria Center of the University of Rennes - Petri/Turing room

    Speaker : Nesrine Kaaniche - Télécom SudParis

    Federated Learning (FL) enables the distributed training of a model across multiple data owners under the orchestration of a central server responsible for aggregating the models generated by the different clients. However, the original approach of FL has significant shortcomings related to privacy and fairness requirements. Specifically, the observation of the model updates may lead to privacy[…]

    • Cryptography

    • SoSysec

    • Privacy

    • Machine learning

  • NEAT: A Nile-English Aligned Translation Corpus based on a Robust Methodology for Intent Based Networking and Security

    • September 26, 2025 (11:00 - 12:00)

    • Inria Center of the University of Rennes - Room Métivier

    Speaker : Pierre Alain - IUT de Lannion

    The rise of Intent Based Networking (IBN) has paved the way for more efficient network and security management, reduced errors, and accelerated deployment times by leveraging AI processes capable of translating natural language intents into policies or configurations. Specialized neural networks could offer a promising solution at the core of translation operations. Still, they require dedicated,[…]

    • SoSysec

    • Network

    • Security policies

  • Black-Box Collision Attacks on Widely Deployed Perceptual Hash Functions and Their Consequences

    • October 03, 2025 (11:00 - 12:00)

    • Inria Center of the University of Rennes - Métivier room

    Speaker : Diane Leblanc-Albarel - KU Leuven

    Perceptual hash functions identify multimedia content by mapping similar inputs to similar outputs. They are widely used for detecting copyright violations and illegal content but lack transparency, as their design details are typically kept secret. Governments are considering extending the application of these functions to Client-Side Scanning (CSS) for end-to-end encrypted services: multimedia[…]

    • Cryptography

    • SoSysec

  • Malware Detection with AI Systems: bridging the gap between industry and academia

    • October 09, 2025 (11:00)

    • Inria Center of the University of Rennes - Room Aurigny

    Speaker : Luca Demetrio - University of Genova

    With the abundance of programs developed everyday, it is possible to develop next-generation antivirus programs that leverage this vast accumulated knowledge. In practice, these technologies are developed with a mixture of established techniques like pattern matching, and machine learning algorithms, both tailored to achieve high detection rate and low false alarms. While companies state the[…]

    • SoSysec

    • Intrusion detection

    • Machine learning

Show previous sessions
Page top