Description
We propose a new formal criterion for secure compilation, providing strong end-to-end security guarantees for components written in unsafe, low-level languages with C-style undefined behavior. Our criterion is the first to model dynamic compromise in a system of mutually distrustful components running with least privilege. Each component is protected from all the others until it becomes compromised by exhibiting undefined behavior, opening the door for an attacker to take control over the component and to use the component's privileges to attack the remaining uncompromised components. More precisely, we ensure that dynamically compromised components cannot break the safety properties of the system at the target level any more than equally privileged components without undefined behavior already could in the source language. To illustrate this model, we build a secure compilation chain for an unsafe language with buffers, procedures, and components. We compile this to a simple RISC abstract machine with built-in compartmentalization and provide machine-checked proofs in Coq showing that this compiler satisfies our secure compilation criterion. Finally, we show that the protection guarantees offered by the compartmentalized abstract machine can be achieved at the machine-code level using either software fault isolation or tag-based reference monitoring."
Infos pratiques
Prochains exposés
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Towards privacy-preserving and fairness-aware federated learning framework
Orateur : 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
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SoSysec
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Privacy
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Machine learning
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NEAT: A Nile-English Aligned Translation Corpus based on a Robust Methodology for Intent Based Networking and Security
Orateur : 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
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Network
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Security policies
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Black-Box Collision Attacks on Widely Deployed Perceptual Hash Functions and Their Consequences
Orateur : 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
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SoSysec
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Malware Detection with AI Systems: bridging the gap between industry and academia
Orateur : 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
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Intrusion detection
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Machine learning
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