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Stefan Mangard

Researcher at Graz University of Technology

Publications -  195
Citations -  17568

Stefan Mangard is an academic researcher from Graz University of Technology. The author has contributed to research in topics: Side channel attack & Encryption. The author has an hindex of 56, co-authored 179 publications receiving 14723 citations. Previous affiliations of Stefan Mangard include Infineon Technologies.

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Book ChapterDOI

Statistical Ineffective Fault Attacks on Masked AES with Fault Countermeasures

TL;DR: It is shown how to attack implementations protected with both masking and detection-based fault countermeasures by using statistical ineffective fault attacks using a single fault induction per execution to show that the combination of masking plus error detection alone may not provide sufficient protection against implementation attacks.
Journal ArticleDOI

An IoT Endpoint System-on-Chip for Secure and Energy-Efficient Near-Sensor Analytics

TL;DR: In this article, the authors proposed Fulmine, a system-on-chip (SoC) based on a tightly-coupled multi-core cluster augmented with specialized blocks for compute-intensive data processing and encryption functions.
Book ChapterDOI

Practical Memory Deduplication Attacks inźSandboxed Javascript

TL;DR: This work presents the first memory-disclosure attack in sandboxed Javascript which exploits page deduplication, and is not only able to determine which applications are running, but also specific user activities, for instance, whether the user has specific websites currently opened.
Journal Article

A simple power-analysis (SPA) attack on implementations of the AES key expansion

TL;DR: This article presents a simple power-analysis (SPA) attack on implementations of the AES key expansion that efficiently utilizes information leakage to substantially reduce the key space that needs to be considered in a brute-force search for the secret key.
Proceedings ArticleDOI

TIMBER-V: Tag-Isolated Memory Bringing Fine-grained Enclaves to RISC-V

TL;DR: TIMBER-V is a new tagged memory architecture featuring flexible and efficient isolation of code and data on small embedded systems, and significantly reduces the problem of memory fragmentation, and improves dynamic reuse of untrusted memory across security boundaries.