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- Open Access (6)
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- ASIC (1)
- Address-bit attacks (1)
- Artificial intelligence (1)
- Atomicity principle (1)
- Continuum robots (1)
- Elliptic curve cryptography (1)
- Elliptic curves (1)
- FPGA (1)
- Horizontal attacks (1)
- NIST (1)
- Resilience (1)
- Robots (1)
- Side-channel Analysis (SCA) (1)
- Simple Power Analysis (SPA) (1)
- horizontal attack (1)
- machine learning (1)
- non-profiled attack (1)
- side channel analysis (1)
Institut
BTU
Unifizierter Multiplizierer
(2010)
Unifizierter Multiplizierer
(2011)
RESCUE: Interdependent Challenges of Reliability, Security and Quality in Nanoelectronic Systems
(2019)
Many technical solutions are bio-inspired. Octopus-inspired robotic arms belong to continuum robots which are used in minimally invasive surgery or for technical system restoration in areas difficult-to-access. Continuum robot missions are bounded with their motions, whereby the motion of the robots is controlled by humans via wireless communication. In case of a lost connection, robot autonomy is required. Distributed control and distributed decision-making mechanisms based on artificial intelligence approaches can be a promising solution to achieve autonomy of technical systems and to increase their resilience. However these methods are not well investigated yet. Octopuses are the living example of natural distributed intelligence but their learning and decision-making mechanisms are also not fully investigated and understood yet. Our major interest is investigating mechanisms of Distributed Artificial Intelligence as a basis for improving resilience of complex systems. We decided to use a physical continuum robot prototype that is able to perform some basic movements for our research. The idea is to research how a technical system can be empowered to combine movements into sequences of motions by itself. For the experimental investigations a suitable physical prototype has to be selected, its motion control has to be implemented and automated. In this paper, we give an overview combining different fields of research, such as Distributed Artificial Intelligence and continuum robots based on 98 publications. We provide a detailed description of the basic motion control models of continuum robots based on the literature reviewed, discuss different aspects of autonomy and give an overview of physical prototypes of continuum robots.
Impact of Data Preparation in Freezing of Gait Detection using Feature-Less Recurrent Neural Network
(2021)
Sensitivity of Standard Library Cells to Optical Fault Injection Attacks in IHP 250 nm Technology
(2020)
Efficient Implementation of Unified ECC Accelerators based on the Karatsuba Multiplication Method
(2021)
Multiplier as a Mean for Reducing Vulnerability of Atomic Patterns to Horizontal Address-Bit Attacks
(2021)
Plasma - A Middleware for Location-Based Services: Design, Implementations and Lessons Learned
(2004)
In this work we discuss the vulnerability of atomic pattern algorithms for elliptic curve point multiplication against simple side-channel analysis attacks using our own implementation as an example. One of the assumptions, on which the atomicity principle is based, is the indistinguishability of operations with different registers, i.e. storing of the data into two different registers cannot be distinguished if their old and new data values are the same. But before the data can be stored in a register/block, this register/block has to be addressed for storing the data. Different registers/blocks have different addresses. The key-dependent addressing of registers/blocks is an inherent feature of the binary kP algorithms and allows to reveal the key k. In our work we demonstrated it. This means that the main assumption, that addressing of different registers/blocks is an indistinguishable operation, may no longer be applied when realizing kP implementations, at least not for hardware implementations.
While IoT technology makes industries, cities, and homes smarter, it also opens the door to security risks. With the right equipment and physical access to the devices, the attacker can leverage side-channel information, like timing, power consumption, or electromagnetic emanation, to compromise cryptographic operations and extract the secret key. This work presents a side channel analysis of a cryptographic hardware accelerator for the Elliptic Curve Scalar Multiplication operation, implemented in a Field-Programmable Gate Array and as an Application-Specific Integrated Circuit. The presented framework consists of initial key extraction using a state-of-the-art statistical horizontal attack and is followed by regularized Artificial Neural Networks, which take, as input, the partially incorrect key guesses from the horizontal attack and correct them iteratively. The initial correctness of the horizontal attack, measured as the fraction of correctly extracted bits of the secret key, was improved from 75% to 98% by applying the iterative learning.