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Adversarial Attack Protection Scalar Multiplication for WSNs Resistance Machine-Learning Side-channel Attack

Adversarial Attack Protection Scalar Multiplication for WSNs Resistance Machine-Learning... Wireless sensor networks (WSNs) have limited storage and low processing capabilities. However, these devices may be captured by opponents, so the security requirements are particularly strict. With the development of side-channel attacks based on the machine-learning, designing a safe elliptic curve scalar multiplication for computing-limited devices like WSNs has become a major challenge. Based on the adversarial attack technology, a secure scalar multiplication is proposed in this article. The main contributions are: (1) We propose an efficient non-zero form (NZF) encoding algorithm that can be applied to various types of elliptic curves; (2) we have designed a secure scalar multiplication algorithm that can resist against conventional side-channel attacks such as SPA, DA, DPA, RPA, and ZPA; and (3) we propose an adversarial protection mechanism based on blind point technology and NZF coding, which can prevent side-channel attacks based on machine learning. The algorithm has no precomputation and is suitable for low communication frequency, low calculation amount, and high security requirements. Especially, it can be applied to lightweight equipment such as WSN and IoT. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Sensor Networks (TOSN) Association for Computing Machinery

Adversarial Attack Protection Scalar Multiplication for WSNs Resistance Machine-Learning Side-channel Attack

Yang, Weizhong
ACM Transactions on Sensor Networks (TOSN) , Volume 18 (3): 13 – Jun 2, 2022
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Publisher
Association for Computing Machinery
Copyright
Copyright © 2022 Copyright held by the owner/author(s). Publication rights licensed to ACM.
ISSN
1550-4859
eISSN
1550-4867
DOI
10.1145/3486679
Publisher site
See Article on Publisher Site

Abstract

Wireless sensor networks (WSNs) have limited storage and low processing capabilities. However, these devices may be captured by opponents, so the security requirements are particularly strict. With the development of side-channel attacks based on the machine-learning, designing a safe elliptic curve scalar multiplication for computing-limited devices like WSNs has become a major challenge. Based on the adversarial attack technology, a secure scalar multiplication is proposed in this article. The main contributions are: (1) We propose an efficient non-zero form (NZF) encoding algorithm that can be applied to various types of elliptic curves; (2) we have designed a secure scalar multiplication algorithm that can resist against conventional side-channel attacks such as SPA, DA, DPA, RPA, and ZPA; and (3) we propose an adversarial protection mechanism based on blind point technology and NZF coding, which can prevent side-channel attacks based on machine learning. The algorithm has no precomputation and is suitable for low communication frequency, low calculation amount, and high security requirements. Especially, it can be applied to lightweight equipment such as WSN and IoT.

Journal

ACM Transactions on Sensor Networks (TOSN) – Association for Computing Machinery

Published: Jun 2, 2022

Keywords: Scalar multiplication

References

  • Zero-value point attacks on elliptic curve cryptosystem
    [,
  • Branch prediction attack on blinded scalar multiplication
    [,
  • Deep learning to evaluate secure RSA implementations
    [,
  • Low-cost solutions for preventing simple side-channel analysis: Side-channel atomicity
    [,
  • Resistance against differential power analysis for elliptic curve cryptosystems
    [,
  • Breaking randomized mixed-radix scalar multiplication algorithms
    [,
  • Side-channel analysis on blinded regular scalar multiplications
    [,
  • The doubling attack—Why upwards is better than downwards
    [,
  • A refined power-analysis attack on elliptic curve cryptosystems
    [,
  • Randomized mixed-radix scalar multiplication
    [,
  • A lightweight ECC-based authentication scheme for internet of things (IoT)
    [,
  • Single-trace side-channel attacks on scalar multiplications with precomputations
    [,
  • Elliptic curves and side-channel analysis
    [,
  • Protections against differential analysis for elliptic curve cryptography—an algebraic approach
    [,
  • Elliptic curve cryptosystems
    [,
  • Timing attacks on implementations of diffifie-hellman, rsa, dss, and other systems
    [,
  • Differential power analysis
    [,
  • A survey on energy-efficient strategies in static wireless sensor networks
    [,
  • IoT-NUMS: Evaluating NUMS elliptic curve cryptography for IoT platforms
    [,
  • Fast and flexible elliptic curve point arithmetic over prime fields
    [,
  • Energy efficient cluster-based routing protocol for WSN using butterfly optimization algorithm and ant colony optimization
    [,
  • Use of elliptic curves in cryptography
    [,
  • Universal adversarial perturbations
    [,
  • Applying horizontal clustering side-channel attacks on embedded ECC implementations
    [,
  • Attacking embedded ECC implementations through cmov side channels
    [,
  • Template attacks using classification algorithms
    [,
  • Fast and regular algorithms for scalar multiplication over elliptic curves
    [,
  • Side-channel attacks on blinded scalar multiplications revisited
    [,
  • Exponent blinding does not always lift (partial) spa resistance to higher-level security
    [,
  • Power attacks in the presence of exponent blinding
    [,
  • Efficient side-channel attacks on scalar blinding on elliptic curves with special structure
    [,
  • Generic power attacks on RSA with CRT and exponent blinding: New results
    [,
  • Intriguing properties of neural networks
    [,
  • One trace is all it takes: Machine learning-based side-channel attack on EdDSA
    [,