Zoya Dyka

The influence of redundant implementations on success of physical attacks against cryptographic devices is currently under-researched. This is especially an issue in application fields such as wearable health, industrial control systems and the like in which devices are accessible to potential attackers. This paper presents results of an investigation of the TMR application impact on the vulnerability of FPGA-based asymmetric cryptographic accelerators to side-channel analysis attacks. We implemented our cryptographic cores using full- and partial-TMR application approaches and experimentally conducted evaluation of their side-channel resistance. Our results reveal that TMR can significantly impact side-channel leakage, either increasing resistance by introducing noise or amplifying leakage depending on the part of the design where redundancy was applied.

In this work, we provide an overview of our front-side Fault Injection (FI) experiments with different logic cells manufactured in two IHP BiCMOS technologies using Riscure equipment for laser FIs. We were able to inject faults into different types of cells including standard library cells as well as into two types of radiation tolerant flip-flops. Experimenting with radiation-tolerant flip-flops faults were injected illuminating areas with PMOS transistors in OFF state. We determined the cells areas, which were sensitive to the laser FI attacks. Only few works discussed this aspect in the past determining NMOS transistors as the sensitive part of the logic cells. Knowledge about the areas which are sensitive to the laser FI attacks can be generalized experimenting with other technologies and used in future by designers to implement corresponding countermeasure(s) at the initial stage of chip development.

Devices employing cryptographic approaches have to be resistant to physical attacks. Side-Channel Analysis (SCA) and Fault Injection (FI) attacks are frequently used to reveal cryptographic keys. In this paper, we present a combined SCA and laser illumination attack against an Elliptic Curve Scalar Multiplication accelerator, while using different equipment for the measurement of its power traces, i.e., we performed the measurements using a current probe from Riscure and a differential probe from Teledyne LeCroy, with an attack success of 70% and 90%, respectively. Our experiments showed that laser illumination increased the power consumption of the chip, especially its static power consumption, but the success of the horizontal power analysis attacks changed insignificantly. After applying 100% of the laser beam output power and illuminating the smallest area of 143 µm², we observed an offset of 17 mV in the measured trace. We assume that using a laser with a high laser beam power, as well as concentrating on measuring and analysing only static current, can significantly improve the attack’s success. The attacks exploiting the Static Current under Laser Illumination (SCuLI attacks) are novel, and their potential has not yet been fully investigated. These attacks can be especially dangerous against cryptographic chips manufactured in downscaling technologies. If such attacks are feasible, appropriate countermeasures have to be proposed in the future.

Detection and prediction of the onset of seizures are among the most challenging problems in epilepsy diagnostics and treatment. Small electronic devices capable of doing that will improve the quality of life for epilepsy patients while also open new opportunities for pharmacological intervention. This paper presents a novel approach using machine learning techniques to detect seizures onset using intracranial electroencephalography (EEG) signals. The proposed approach was tested on intracranial EEG data recorded in rats with pilocarpine model of temporal lobe epilepsy. A principal component analysis was applied for feature selection before using a support vector machine for the detection of seizures. Hjorth’s parameters and Daubechies discrete wavelet transform coefficients were found to be the most informative features of EEG data. We found that the support vector machine approach had a classification sensitivity of 90% and a specificity of 74% for detecting ictal episodes. Changing the epoch parameter from one to twenty-one seconds results in changing the redistribution of principal components’ values to 10% but does not affect the classification result. Support vector machines are accessible and convenient methods for classification that have achieved promising classification quality, and are rather lightweight compared to other machine learning methods. So we suggest their future use in mobile devices for early epileptic seizure and preictal episodes detection.

Elliptic Curve Scalar Multiplication denoted as kP operation is the basic operation in all Elliptic Curve based cryptographic protocols. The atomicity principle and different atomic patterns for kP algorithms were proposed in the past as countermeasures against simple side-channel analysis. In this work, we investigated the resistance of a kP algorithm implemented in hardware using Longa's atomic patterns. We analysed its simulated power trace. We show in the example of our kP implementation for the NIST EC P-256 that the field squaring operations are distinguishable from the field multiplications even if they are performed by the same field multiplier, due to the addressing of the second multiplicand. This inherent vulnerability of atomic patterns can be successfully exploited for revealing the scalar k.

Security especially in the fields of IoT, industrial automation and critical infrastructure is paramount nowadays and a hot research topic. In order to ensure confidence in research results they need to be reproducible. In the past we reported [18] that in many publications important information such as details about the equipment used are missing. In this paper we report on our own experiments that we run to verify the parameters reported in the datasheets that came along with our experimental equipment. Our results show that there are significant discrepancies between the datasheets and the real world data. These deviations concern accuracy of positions, movements, duration of laser shots etc. In order to improve reproducibility of results we therefore argue on the one hand that research groups verify the data given in datasheets of equipment they use and on the other hand that they provide measurement set-up parameters in globally accepted units such as cm, seconds, etc.

Physical side-channel attacks represent a great challenge for today's chip design. Although attacks on CMOS dynamic power represent a class of state-of-the-art attacks, many other effects potentially affect the security of CMOS chips analogously by affecting mostly static behaviour of the chip, including aging, ionizing radiation, or non-ionizing illumination of the CMOS. Vulnerabilities exploiting data dependency in CMOS static power were already demonstrated in practice and the analogous vulnerability exploiting light-modulated static power was demonstrated by simulation. This work confirms the CMOS vulnerability related to the light-modulated data-dependent static power experimentally and discusses future work.

Using sensors as a means to achieve self-awareness and artificial intelligence for decision-making, may be a way to make complex systems self-adaptive, autonomous and resilient. Investigating the combination of distributed artificial intelligence methods and bio-inspired robotics can provide results that will be helpful for implementing autonomy of such robots and other complex systems. In this paper, we describe Distributed Artificial Intelligence application area, the most common examples of continuum robots and provide a description of our first steps towards implementing distributed control.

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.

There are different ways to leverage Side Channel information into a successful attack against cryptographic hardware. The most constraint attack scenario assumes no knowledge about the internal states of the hardware during secret key processing and therefore provides no labels for power hungry deep learning algorithms, which are the state of the art in profiling attacks. In our non-profiled single-trace attack we used a statistical method the comparison to the mean to retrieve the initial key candidates and trained a highly regularized Support Vector Machine (SVM) using those candidates. We achieved an improvement in attack correctness of about 10 ^% between the initial and final key candidates. We attacked two implementations of Elliptic Curve Scalar Multiplication. One is an ASIC produced in the IHP 250nm technology, where the hardware description was compiled using the compile_ultra option, which has been proven to increase the resistance against SCA attacks. Another one is an FPGA implementation, using the sequential addressing countermeasure, which minimizes the number of clock cycles with bus addressing leakage.