scholarly journals Side Channel Assessment Platforms and Tools for Ubiquitous Systems

2021 ◽  
pp. 147-163
Author(s):  
Apostolos P. Fournaris ◽  
Athanassios Moschos ◽  
Nicolas Sklavos
Keyword(s):  
Open Source ◽  
Control Mechanism ◽  
Side Channel ◽  
Slow Process ◽  
Considerable Time ◽  
Side Channel Attacks ◽  
Ubiquitous Systems ◽  
Need To Evaluate ◽  
Implementation Under Test

AbstractSide Channel Attacks are nowadays considered a serious risk for many security products and ubiquitous devices. Strong security solution providers need to evaluate their implementations against such attacks before publishing them on the market, thus performing a thorough assessment. However, this procedure is not straightforward and even with the appropriate equipment, it may require considerable time to provide results due to the slow process of collecting measurements (traces) and the inflexible way of controlling the tested implementation. In this chapter, we explore and overview the trace collection landscape for generic devices under test (including ubiquitous systems) highlighting and overviewing the latest trace collection toolsets and their shortcomings, but also proposing a trace collection approach that can be applied on the most recent, open source toolsets. We showcase our proposed approach on the FlexLeco project architecture, which we have developed in our lab, and manage to practically describe how an evaluator using the proposed methodology can collect traces easily and quickly without the need to completely redesign a control mechanism for the implementation under test.

scholarly journals Share-slicing: Friend or Foe?

2019 ◽  
pp. 152-174
Author(s):  
Si Gao ◽  
Ben Marshall ◽  
Dan Page ◽  
Elisabeth Oswald
Keyword(s):  
Open Source ◽  
Real World ◽  
Noise Level ◽  
Side Channel ◽  
Side Channel Attacks ◽  
Great Cost ◽  
Architectural Features ◽  
Security Guarantees ◽  
Very High ◽  
Made In

Masking is a well loved and widely deployed countermeasure against side channel attacks, in particular in software. Under certain assumptions (w.r.t. independence and noise level), masking provably prevents attacks up to a certain security order and leads to a predictable increase in the number of required leakages for successful attacks beyond this order. The noise level in typical processors where software masking is used may not be very high, thus low masking orders are not sufficient for real world security. Higher order masking however comes at a great cost, and therefore a number techniques have been published over the years that make such implementations more efficient via parallelisation in the form of bit or share slicing. We take two highly regarded schemes (ISW and Barthe et al.), and some corresponding open source implementations that make use of share slicing, and discuss their true security on an ARM Cortex-M0 and an ARM Cortex-M3 processor (both from the LPC series). We show that micro-architectural features of the M0 and M3 undermine the independence assumptions made in masking proofs and thus their theoretical guarantees do not translate into practice (even worse it seems unpredictable at which order leaks can be expected). Our results demonstrate how difficult it is to link theoretical security proofs to practical real-world security guarantees.

scholarly journals Side-Channel Countermeasures’ Dissection and the Limits of Closed Source Security Evaluations

2020 ◽  
pp. 1-25 ◽  
Author(s):  
Olivier Bronchain ◽  
François-Xavier Standaert
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Side Channel ◽  
Learning Tools ◽  
Side Channel Attacks ◽  
Data Dependencies ◽  
Security Evaluations ◽  
Systèmes D’Information ◽  
Closed Source ◽  
Leakage Assessment

We take advantage of a recently published open source implementation of the AES protected with a mix of countermeasures against side-channel attacks to discuss both the challenges in protecting COTS devices against such attacks and the limitations of closed source security evaluations. The target implementation has been proposed by the French ANSSI (Agence Nationale de la Sécurité des Systèmes d’Information) to stimulate research on the design and evaluation of side-channel secure implementations. It combines additive and multiplicative secret sharings into an affine masking scheme that is additionally mixed with a shuffled execution. Its preliminary leakage assessment did not detect data dependencies with up to 100,000 measurements. We first exhibit the gap between such a preliminary leakage assessment and advanced attacks by demonstrating how a countermeasures’ dissection exploiting a mix of dimensionality reduction, multivariate information extraction and key enumeration can recover the full key with less than 2,000 measurements. We then discuss the relevance of open source evaluations to analyze such implementations efficiently, by pointing out that certain steps of the attack are hard to automate without implementation knowledge (even with machine learning tools), while performing them manually is straightforward. Our findings are not due to design flaws but from the general difficulty to prevent side-channel attacks in COTS devices with limited noise. We anticipate that high security on such devices requires significantly more shares.

Countermeasure for Cryptographic Chips to Resist Side-Channel Attacks

2009 ◽  
Vol 19 (11) ◽  
pp. 2990-2998 ◽  
Author(s):  
Tao ZHANG ◽  
Ming-Yu FAN
Keyword(s):  
Side Channel ◽  
Side Channel Attacks

scholarly journals ThermalAttackNet: Are CNNs Making It Easy to Perform Temperature Side-Channel Attack in Mobile Edge Devices?

2021 ◽  
Vol 13 (6) ◽  
pp. 146
Author(s):  
Somdip Dey ◽  
Amit Kumar Singh ◽  
Klaus McDonald-Maier
Keyword(s):  
Information Flow ◽  
Heat Dissipation ◽  
Side Channel ◽  
Side Channel Attack ◽  
Side Channel Attacks ◽  
Information Flow Control ◽  
On Chip ◽  
Temperature Side ◽  
Over Time ◽  
Memory Efficient

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.

Power Side-Channel Analysis of RNS GLV ECC Using Machine and Deep Learning Algorithms

2021 ◽  
Vol 21 (3) ◽  
pp. 1-20
Author(s):  
Mohamad Ali Mehrabi ◽  
Naila Mukhtar ◽  
Alireza Jolfaei
Keyword(s):  
Deep Learning ◽  
Elliptic Curve ◽  
Smart Cities ◽  
Information Leakage ◽  
Side Channel ◽  
Side Channel Attacks ◽  
Public Key Cryptosystems ◽  
Elliptic Curve Cryptosystems ◽  
Hardware Implementations ◽  
Substantial Progress

Many Internet of Things applications in smart cities use elliptic-curve cryptosystems due to their efficiency compared to other well-known public-key cryptosystems such as RSA. One of the important components of an elliptic-curve-based cryptosystem is the elliptic-curve point multiplication which has been shown to be vulnerable to various types of side-channel attacks. Recently, substantial progress has been made in applying deep learning to side-channel attacks. Conceptually, the idea is to monitor a core while it is running encryption for information leakage of a certain kind, for example, power consumption. The knowledge of the underlying encryption algorithm can be used to train a model to recognise the key used for encryption. The model is then applied to traces gathered from the crypto core in order to recover the encryption key. In this article, we propose an RNS GLV elliptic curve cryptography core which is immune to machine learning and deep learning based side-channel attacks. The experimental analysis confirms the proposed crypto core does not leak any information about the private key and therefore it is suitable for hardware implementations.

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