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

Mohamad Ali Mehrabi, Naila Mukhtar, Alireza Jolfaei

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Article number65
Number of pages20
JournalACM Transactions on Internet Technology
Volume21
Issue number3
DOIs
Publication statusPublished - Aug 2021
Externally publishedYes

Keywords

  • convolutional neural networks
  • deep learning
  • Elliptic curve cryptography
  • Gallant-Lambert-Vanstone (GLV) point multiplication
  • machine learning
  • side-channel attacks

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