Technology Category
Machine Learning: Adversarial Learning & RobustnessComputer Vision: Adversarial Attacks & RobustnessKnowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
Deep Learning-Assisted Improved Differential Fault Attacks on Lightweight Stream Ciphers
🤖 AI Summary
This study addresses the challenge of key recovery for lightweight stream ciphers under unknown single-bit random faults in resource-constrained devices. By integrating deep learning with differential fault analysis, the authors propose a multilayer perceptron (MLP)-based approach for precise fault location identification and introduce a thresholding strategy to enhance key recovery efficiency. The work presents the first experimental differential fault attack on the ATOM cipher, achieving fault localization accuracies of 0.999880 for ACORNv3, 0.999231 for MORUSv2, and 0.823568 for ATOM. Remarkably, the initial state of ACORN can be recovered with only 21–34 induced faults, while MORUS requires 213–248 faults with at most 6 bits guessed, substantially outperforming existing methods.
Technology Category
Application Category
📝 Abstract
Lightweight cryptographic primitives are widely deployed in resource-constraint environment, particularly in the Internet of Things (IoT) devices. Due to their public accessibility, these devices are vulnerable to physical attacks, especially fault attacks. Recently, deep learning-based cryptanalytic techniques have demonstrated promising results; however, their application to fault attacks remains limited, particularly for stream ciphers. In this work, we investigate the feasibility of deep learning assisted differential fault attack on three lightweight stream ciphers, namely ACORNv3, MORUSv2 and ATOM, under a relaxed fault model, where a single-bit bit-flipping fault is injected at an unknown location. We train multilayer perceptron (MLP) models to identify the fault locations. Experimental results show that the trained models achieve high identification accuracies of 0.999880, 0.999231 and 0.823568 for ACORNv3, MORUSv2 and ATOM, respectively, and outperform traditional signature-based methods. For the secret recovery process, we introduce a threshold-based method to optimize the number of fault injections required to recover the secret information. The results show that the initial state of ACORN can be recovered with 21 to 34 faults; while MORUS requires 213 to 248 faults, with at most 6 bits of guessing. Both attacks reduce the attack complexity compared to existing works. For ATOM, the results show that it possesses a higher security margin, as majority of state bits in the Non-linear Feedback Shift Register (NFSR) can only be recovered under a precise control model. To the best of our knowledge, this work provides the first experimental results of differential fault attacks on ATOM.
Problem
Research questions and friction points this paper is trying to address.
differential fault attack
lightweight stream ciphers
deep learning
fault location identification
secret recovery
Innovation
Methods, ideas, or system contributions that make the work stand out.
deep learning
differential fault attack
lightweight stream cipher
fault localization
threshold-based optimization
🔎 Similar Papers
No similar papers found.
