Empirical evaluation of multi-device profiling side-channel attacks

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.

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Performance Analysis of Non-Profiled Side Channel Attacks Based on Convolutional Neural Networks

2020 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS) ◽

10.1109/apccas50809.2020.9301673 ◽

2020 ◽

Author(s):

Ngoc-Tuan Do ◽

Van-Phuc Hoang ◽

Van-Sang Doan

Keyword(s):

Neural Networks ◽

Performance Analysis ◽

Convolutional Neural Networks ◽

Side Channel ◽

Side Channel Attacks

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HybriDG: Hybrid Dynamic Time Warping and Gaussian Distribution Model for Detecting Emerging Zero-Day Microarchitectural Side-Channel Attacks

2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA) ◽

10.1109/icmla51294.2020.00101 ◽

2020 ◽

Author(s):

Han Wang ◽

Hossein Sayadi ◽

Avesta Sasan ◽

Setareh Rafatirad ◽

Houman Homayoun

Keyword(s):

Gaussian Distribution ◽

Dynamic Time Warping ◽

Distribution Model ◽

Side Channel ◽

Time Warping ◽

Side Channel Attacks ◽

Dynamic Time ◽

Gaussian Distribution Model

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

ACM Transactions on Internet Technology ◽

10.1145/3423555 ◽

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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