Automated Generation of Masked Hardware

Author(s):  
David Knichel ◽  
Amir Moradi ◽  
Nicolai Müller ◽  
Pascal Sasdrich

Masking has been recognized as a sound and secure countermeasure for cryptographic implementations, protecting against physical side-channel attacks. Even though many different masking schemes have been presented over time, design and implementation of protected cryptographic Integrated Circuits (ICs) remains a challenging task. More specifically, correct and efficient implementation usually requires manual interactions accompanied by longstanding experience in hardware design and physical security. To this end, design and implementation of masked hardware often proves to be an error-prone task for engineers and practitioners. As a result, our novel tool for automated generation of masked hardware (AGEMA) allows even inexperienced engineers and hardware designers to create secure and efficient masked cryptograhic circuits originating from an unprotected design. More precisely, exploiting the concepts of Probe-Isolating Non-Interference (PINI) for secure composition of masked circuits, our tool provides various processing techniques to transform an unprotected design into a secure one, eventually accelerating and safeguarding the process of masking cryptographic hardware. Ultimately, we evaluate our tool in several case studies, emphasizing different trade-offs for the transformation techniques with respect to common performance metrics, such as latency, area, andrandomness.

2021 ◽  
Vol 13 (6) ◽  
pp. 146
Author(s):  
Somdip Dey ◽  
Amit Kumar Singh ◽  
Klaus McDonald-Maier

Side-channel attacks remain a challenge to information flow control and security in mobile edge devices till this date. One such important security flaw could be exploited through temperature side-channel attacks, where heat dissipation and propagation from the processing cores are observed over time in order to deduce security flaws. In this paper, we study how computer vision-based convolutional neural networks (CNNs) could be used to exploit temperature (thermal) side-channel attack on different Linux governors in mobile edge device utilizing multi-processor system-on-chip (MPSoC). We also designed a power- and memory-efficient CNN model that is capable of performing thermal side-channel attack on the MPSoC and can be used by industry practitioners and academics as a benchmark to design methodologies to secure against such an attack in MPSoC.


2020 ◽  
Author(s):  
Somdip Dey ◽  
Amit Kumar ◽  
Klaus D. Mcdonald-Maier

<div><div><div><p>Side-channel attacks remain a challenge to information flow control and security in mobile edge devices till this date. One such important security flaw could be exploited through temperature side-channel attacks, where heat dissipation and propagation from the processing cores are observed over time in order to deduce security flaws. In this brief, we study how computer vision based convolutional neural networks (CNNs) could be used to exploit temperature (thermal) side-channel attack on different Linux governors in mobile edge device utilizing multi- processor system-on-chip (MPSoC). We also designed a power- and memory-efficient CNN model that is capable of performing thermal side-channel attack on the MPSoC and can be used by industry practitioners and academics as a benchmark to design methodologies to secure against such an attack in MPSoC.</p></div></div></div>


2020 ◽  
Author(s):  
Somdip Dey ◽  
Amit Kumar ◽  
Klaus D. Mcdonald-Maier

<div><div><div><p>Side-channel attacks remain a challenge to information flow control and security in mobile edge devices till this date. One such important security flaw could be exploited through temperature side-channel attacks, where heat dissipation and propagation from the processing cores are observed over time in order to deduce security flaws. In this brief, we study how computer vision based convolutional neural networks (CNNs) could be used to exploit temperature (thermal) side-channel attack on different Linux governors in mobile edge device utilizing multi- processor system-on-chip (MPSoC). We also designed a power- and memory-efficient CNN model that is capable of performing thermal side-channel attack on the MPSoC and can be used by industry practitioners and academics as a benchmark to design methodologies to secure against such an attack in MPSoC.</p></div></div></div>


Author(s):  
Lauren De Meyer ◽  
Amir Moradi ◽  
Felix Wegener

The effort in reducing the area of AES implementations has largely been focused on Application-Specific Integrated Circuits (ASICs) in which a tower field construction leads to a small design of the AES S-box. In contrast, a naïve implementation of the AES S-box has been the status-quo on Field-Programmable Gate Arrays (FPGAs). A similar discrepancy holds for masking schemes – a wellknown side-channel analysis countermeasure – which are commonly optimized to achieve minimal area in ASICs.In this paper we demonstrate a representation of the AES S-box exploiting rotational symmetry which leads to a 50% reduction of the area footprint on FPGA devices. We present new AES implementations which improve on the state of the art and explore various trade-offs between area and latency. For instance, at the cost of increasing 4.5 times the latency, one of our design variants requires 25% less look-up tables (LUTs) than the smallest known AES on Xilinx FPGAs by Sasdrich and Güneysu at ASAP 2016. We further explore the protection of such implementations against first-order side-channel analysis attacks. Targeting the small area footprint on FPGAs, we introduce a heuristic-based algorithm to find a masking of a given function with d + 1 shares. Its application to our new construction of the AES S-box allows us to introduce the smallest masked AES implementation on Xilinx FPGAs, to-date.


Cryptography ◽  
2021 ◽  
Vol 5 (3) ◽  
pp. 16
Author(s):  
Davide Bellizia ◽  
Riccardo Della Sala ◽  
Giuseppe Scotti

With the continuous scaling of CMOS technology, which has now reached the 3 nm node at production level, static power begins to dominate the power consumption of nanometer CMOS integrated circuits. A novel class of security attacks to cryptographic circuits which exploit the correlation between the static power and the secret keys was introduced more than ten years ago, and, since then, several successful key recovery experiments have been reported. These results clearly demonstrate that attacks exploiting static power (AESP) represent a serious threat for cryptographic systems implemented in nanometer CMOS technologies. In this work, we analyze the effectiveness of the Standard Cell Delay-based Precharge Logic (SC-DDPL) style in counteracting static power side-channel attacks. Experimental results on an FPGA implementation of a compact PRESENT crypto-core show that the SC-DDPL implementation allows a great improvement of all the security metrics with respect to the standard CMOS implementation and other state-of-the-art countermeasures such as WDDL and MDPL.


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