Multiple fault analysis using a fault dropping technique

Author(s):  
A. Verreault ◽  
E.M. Aboulhamid ◽  
Y. Karkouri
1994 ◽  
Vol 43 (1) ◽  
pp. 98-103 ◽  
Author(s):  
Y. Karkouri ◽  
E.M. Aboulhamid ◽  
E. Cerny ◽  
A. Verreault

1976 ◽  
Vol 64 (9) ◽  
pp. 1447-1449 ◽  
Author(s):  
S.R. Das ◽  
P.K. Srimani ◽  
C.R. Datta

1975 ◽  
Vol C-24 (1) ◽  
pp. 62-71 ◽  
Author(s):  
Chia-Tai Ku ◽  
G.M. Masson

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1976 ◽  
Author(s):  
Liang Dong ◽  
Hongxin Zhang ◽  
Shaofei Sun ◽  
Lei Zhu ◽  
Xiaotong Cui ◽  
...  

Embedded encryption devices and smart sensors are vulnerable to physical attacks. Due to the continuous shrinking of chip size, laser injection, particle radiation and electromagnetic transient injection are possible methods that introduce transient multiple faults. In the fault analysis stage, the adversary is unclear about the actual number of faults injected. Typically, the single-nibble fault analysis encounters difficulties. Therefore, in this paper, we propose novel ciphertext-only impossible differentials that can analyze the number of random faults to six nibbles. We use the impossible differentials to exclude the secret key that definitely does not exist, and then gradually obtain the unique secret key through inverse difference equations. Using software simulation, we conducted 32,000 random multiple fault attacks on Midori. The experiments were carried out to verify the theoretical model of multiple fault attacks. We obtain the relationship between fault injection and information content. To reduce the number of fault attacks, we further optimized the fault attack method. The secret key can be obtained at least 11 times. The proposed ciphertext-only impossible differential analysis provides an effective method for random multiple faults analysis, which would be helpful for improving the security of block ciphers.


Machines ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 144
Author(s):  
Haodong Yuan ◽  
Nailong Wu ◽  
Xinyuan Chen

For mechanical compound fault, it is of great significance to employ the vibration signal of a single-channel compound fault to analyze and realize the separation of multiple fault sources, which is essentially the problem of single-channel blind source separation. Shift invariant K-means singular value decomposition (shift invariant K-SVD) dictionary learning is suitable to extract the periodic and repeated fault features of a rotating machinery fault, hence in this article a single-channel compound fault analysis method is put forward which combines shift invariant K-SVD with improved fast independent component analysis (improved FastICA) algorithm. Firstly, based on single-channel compound fault signal, the shift invariant K-SVD algorithm can be used for learning multiple latent components that can be constructed as a virtual multi-channel signal. Then the improved FastICA algorithm is utilized to realize the separation of multiple fault source signals. With regard to the FastICA algorithm, the third-order convergence Newton iteration method is adopted to improve convergence speed. Moreover, in order to address the problem that FastICA is very sensitive to initialization, a steepest descent method can be applied. The experimental analysis of the compound fault of rolling bearing verifies that the presented method is effective to separate multiple fault source signals and the improved FastICA algorithm can increase convergence rate and overcome the problem of sensitivity to initialization.


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