HOCOS - Current Research Projects
Algebraic Fault Attacks
Cryptographic circuits are employed in mobile and embedded systems to protect sensitive information from unauthorized access and manipulation. Fault attacks circumvent the protection by injecting faults into the hardware implementation of the cryptographic function, thus manipulating the calculation in a controlled manner and allowing the attacker to derive protected data such as secret keys.
The Algebraic Fault Attacks project focuses on the class of algebraic fault attacks, where the information used for cryptanalysis is represented by systems of polynomials.
Benchmarks for algebraic fault attacks
We are working on creating a comprehensive set of benchmarks for algebraic fault attacks. These will be published here as soon as they are available.
Fault Attack Benchmarks for Small Scale AES
MemCrypto: Towards Secure Electroforming-free Memristive Cryptographic Implementations
Memristive devices offer enormous advantages for non-volatile memories and neuromorphic computing, but there is a rising interest in using memristive technologies for security applications. Project MemCrypto aims at development and investigation of memristive cryptographic implementations, assessment and improvement of their security against physical attacks. This work focuses on combinational and sequential realizations of complete cryptographic circuits and complements earlier research on memristive physical unclonable functions and random number generators.
Near-sensor Computing Using Low-cost Stochastic Circuits
This project aims at developing methods to realize low-cost and power-efficient hardware circuits for near-sensor computing following the Stochastic Computing paradigm. Stochastic computing provides extremely compact, error-tolerant and low-power implementations of complex functions, but at the expense of longer computation times and some degree of inaccuracy. This makes stochastic circuits (SCs) especially attractive for near-sensor computing, where the processed sensor data are inaccurate anyway and computations tend to occur infrequently. A special focus of this project will be the SC realization of neural networks (NNs) used for classification tasks, from lightweight NNs to fully-fledged convolutional NNs for deep learning.
P1: Systematic Analysis of System-Level Test Fails (GS-IMTR)
Test quality, defined as the absence of test escapes (defective circuits that had passed post-manufacturing test), is the ultimate target of testing. Customers apply system-level test (SLT) to circuits that already have been tested post-fabrication and reportedly identify test escapes. The objective of this project is to understand the nature of such hard-to-detect failures. Establishing a better understanding of SLT and making it more effective and efficient could drastically improve the economy of circuit design and manufacturing.