Persistent Fault Attack in Practice

Zhang, Fan; Zhang, Yiran; Jiang, Huilong; Zhu, Xiang; Bhasin, Shivam; Zhao, Xinjie; Liu, Zhe; Gu, Dawu; Ren, Kui

doi:10.46586/tches.v2020.i2.172-195

Cited by 26 publications

(8 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This phenomenon is often called catastrophic amnesia. One of the reasons for the assumption of catastrophic forgetting in neural networks is the change of input distribution in different tasks [12], for example, the lack of common factors or structures may cause the optimization method to converge to completely different results each time. This paper studies the catastrophic forgetting phenomenon that occurs when the model is learning the same task.…”

Section: Related Workmentioning

confidence: 99%

Image Segmentation of Intestinal Polyps using Attention Mechanism based on Convolutional Neural Network

Zheng¹,

Gong²,

Yang³

et al. 2023

IJACSA

View full text Add to dashboard Cite

The intestinal polyp is one of the common intestinal diseases, which is characterized by protruding lining tissue of the colon or rectum. Considering that they may become cancerous, they should be removed by surgery as soon as possible. In the past, it took a lot of manpower and time to identify and diagnose intestinal polyps, which greatly affected the treatment efficiency of medical staff. Because the polyp part looks similar to the normal structure of the human body, the probability of human eye misjudgment is high. Therefore, it is necessary to use advanced computer technology to segment the intestinal polyp image. In the model established in this paper, an image segmentation method based on convolution neural network is proposed. The Har-DNet backbone network is used as the encoder in the model, and its feature processing results are converted into three feature images of different sizes, which are input to the decoding module. In the decoding process, each output first expands the receptive field module and then fuses the feature image processed by the attention mechanism. The fusion results are input to the density aggregation module for processing to improve the operation efficiency and accuracy of the model. The experimental results show that compared with the previous Pra-Net model and Har-DNet MSEG model, the accuracy and precision of this method are greatly improved, and can be applied to the actual medical image recognition process, thus improving the treatment efficiency of patients.

show abstract

Section: Related Workmentioning

confidence: 99%

Image Segmentation of Intestinal Polyps using Attention Mechanism based on Convolutional Neural Network

Zheng¹,

Gong²,

Yang³

et al. 2023

IJACSA

View full text Add to dashboard Cite

show abstract

“…However, it does not mean that PFA is impractical. In [27], Zhang et al solved this problem by adding an extra step to search v and v * before the recovery step. The experimental result demonstrated that the number of ciphertexts to search v and v * is less than the number of ciphertexts to recover k j .…”

Section: Persistent Fault Attackmentioning

confidence: 99%

“…In CHES 2018, Zhang et al proposed the concept of Persistent Fault Attack (PFA) [26]. And in CHES 2020, they demonstrated the feasibility of PFA in practical applications [27]. Unlike other FA methods, the fault model of PFA assumes that the fault in the cryptographic device is persistent rather than transient or permanent.…”

Section: Introductionmentioning

confidence: 99%

A Fast-Detection and Fault-Correction Algorithm against Persistent Fault Attack

Cheng¹,

Zheng²,

Fan³

et al. 2021

Preprint

View full text Add to dashboard Cite

Persistent Fault Attack (PFA) is a recently proposed Fault Attack (FA) method in CHES 2018. It is able to recover full AES secret key in the Single-Byte-Fault scenario. It is demonstrated that classical FA countermeasures, such as Dual Modular Redundancy (DMR) and mask protection, are unable to thwart PFA. In this paper, we propose a fast-detection and faultcorrection algorithm to prevent PFA. We construct a fixed input and output pair to detect faults rapidly. Then we build two extra redundant tables to store the relationship between the adjacent elements in the S-box, by which the algorithm can correct the faulty elements in the S-box. Our experimental results show that our algorithm can effectively prevent PFA in both Single-Byte-Fault and Multiple-Bytes-Faults scenarios. Compared with the classical FA countermeasures, our algorithm has a much better effect against PFA. Further, the time cost of our algorithm is 40% lower than the classical FA countermeasures.

show abstract

“…The current common analysis methods for persistent fault [ 12 , 13 ] are mainly classical persistent fault analysis (PFA) [ 14 , 15 ], enhanced persistent fault analysis (EPFA) [ 16 ], and persistent fault-Based collision analysis (PFCA) [ 17 ]. For the SKINNY block cipher, classical persistent fault analysis cannot recovery the master key.…”

Section: Introductionmentioning

confidence: 99%

Algebraic Persistent Fault Analysis of SKINNY_64 Based on S_Box Decomposition

Fang

Wang

Yan

et al. 2022

Entropy

View full text Add to dashboard Cite

Algebraic persistent fault analysis (APFA), which combines algebraic analysis with persistent fault attacks, brings new challenges to the security of lightweight block ciphers and has received widespread attention since its introduction. Threshold Implementation (TI) is one of the most widely used countermeasures for side channel attacks. Inspired by this method, the SKINNY block cipher adopts the S_box decomposition to reduce the number of variables in the set of algebraic equations and the number of Conjunctive Normal Form (CNF) equations in this paper, thus speeding up the algebraic persistent fault analysis and reducing the number of fault ciphertexts. In our study, we firstly establish algebraic equations for full-round faulty encryption,and then analyze the relationship between the number of fault ciphertexts required and the solving time in different scenarios (decomposed S_boxes and original S_box). By comparing the two sets of experimental results, the success rate and the efficiency of the attack are greatly improved by using S_box decomposition. In this paper, We can recover the master key in a minimum of 2000s using 11 pairs of plaintext and fault ciphertext, while the key recovery cannot be done in effective time using the original S_box expression equations. At the same time, we apply S_box decomposition to another kind of algebraic persistent fault analysis, and the experimental results show that using S_box decomposition can effectively reduce the solving time and solving success rate under the same conditions.

show abstract

Persistent Fault Attack in Practice

Cited by 26 publications

References 10 publications

Image Segmentation of Intestinal Polyps using Attention Mechanism based on Convolutional Neural Network

Image Segmentation of Intestinal Polyps using Attention Mechanism based on Convolutional Neural Network

A Fast-Detection and Fault-Correction Algorithm against Persistent Fault Attack

Algebraic Persistent Fault Analysis of SKINNY_64 Based on S_Box Decomposition

Contact Info

Product

Resources

About