scholarly journals An Effective Simulation Analysis of Transient Electromagnetic Multiple Faults

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1976 ◽  
Author(s):  
Liang Dong ◽  
Hongxin Zhang ◽  
Shaofei Sun ◽  
Lei Zhu ◽  
Xiaotong Cui ◽  
...  
Keyword(s):  
Simulation Analysis ◽  
Fault Analysis ◽  
Differential Analysis ◽  
Secret Key ◽  
Particle Radiation ◽  
Multiple Fault ◽  
Fault Attacks ◽  
Multiple Faults ◽  
Transient Electromagnetic ◽  
Impossible Differentials

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.

scholarly journals Health Condition Estimation of Bearings with Multiple Faults by a Composite Learning-Based Approach

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4424
Author(s):  
Udeme Inyang ◽  
Ivan Petrunin ◽  
Ian Jennions
Keyword(s):  
Fault Diagnosis ◽  
Health Management ◽  
Network Models ◽  
Health Condition ◽  
Limited Attention ◽  
Neural Network Models ◽  
Multiple Fault ◽  
Multiple Faults ◽  
Multiple Fault Diagnosis ◽  
Non Gaussian

Bearings are critical components found in most rotating machinery; their health condition is of immense importance to many industries. The varied conditions and environments in which bearings operate make them prone to single and multiple faults. Widespread interest in the improvements of single fault diagnosis meant limited attention was spent on multiple fault diagnosis. However, multiple fault diagnosis poses extra challenges due to the submergence of the weak fault by the strong fault, presence of non-Gaussian noise, coupling of the frequency components, etc. A number of existing convolutional neural network models operate on a distinct feature that is not enough to assure reliable results in the presence of these challenges. In this paper, extended feature sets in three homogenous deep learning models are used for multiple fault diagnosis. This ensures a measure of diversity is introduced to the health management dataset to obtain complementary solutions from the models. The outputs of the models are fused through blending ensemble learning. Experiments using vibration datasets based on bearing multiple faults show an accuracy of 98.54%, with an improvement of 2.74% in the overall effectiveness over the single models. Compared with other technologies, the results show that this approach provides an improved generalized diagnostic capability.

scholarly journals A New Approach to Multiple Fault Diagnosis: A Combination of Diagnostic Matrices, Graphs, Algebraic and Rule-Based Models. The Case of Two-Layer Models

2008 ◽  
Vol 18 (4) ◽  
pp. 465-476 ◽  
Author(s):  
Antoni Ligęza ◽  
Jan Kościelny
Keyword(s):  
Fault Diagnosis ◽  
Simultaneous Occurrence ◽  
Diagnostic Model ◽  
Rule Based ◽  
New Approach ◽  
Multiple Fault ◽  
Multiple Faults ◽  
Model Based ◽  
Multiple Fault Diagnosis ◽  
System Variables

A New Approach to Multiple Fault Diagnosis: A Combination of Diagnostic Matrices, Graphs, Algebraic and Rule-Based Models. The Case of Two-Layer ModelsThe diagnosis of multiple faults is significantly more difficult than singular fault diagnosis. However, in realistic industrial systems the possibility of simultaneous occurrence of multiple faults must be taken into account. This paper investigates some of the limitations of the diagnostic model based on the simple binary diagnostic matrix in the case of multiple faults. Several possible interpretations of the diagnostic matrix with rule-based systems are provided and analyzed. A proposal of an extension of the basic, single-level model based on diagnostic matrices to a two-level one, founded on causal analysis and incorporating an OR and an AND matrix is put forward. An approach to the diagnosis of multiple faults based on inconsistency analysis is outlined, and a refinement procedure using a qualitative model of dependencies among system variables is sketched out.

scholarly journals Breaking Trivium Stream Cipher Implemented in ASIC Using Experimental Attacks and DFA

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6909
Author(s):  
Francisco Eugenio Potestad-Ordóñez ◽  
Manuel Valencia-Barrero ◽  
Carmen Baena-Oliva ◽  
Pilar Parra-Fernández ◽  
Carlos Jesús Jiménez-Fernández
Keyword(s):  
Stream Cipher ◽  
Internal State ◽  
Clock Cycle ◽  
Fault Analysis ◽  
Invasive Technique ◽  
Sensitive Information ◽  
Secret Key ◽  
Key Recovery ◽  
Differential Fault Analysis ◽  
Secret Keys

One of the best methods to improve the security of cryptographic systems used to exchange sensitive information is to attack them to find their vulnerabilities and to strengthen them in subsequent designs. Trivium stream cipher is one of the lightweight ciphers designed for security applications in the Internet of things (IoT). In this paper, we present a complete setup to attack ASIC implementations of Trivium which allows recovering the secret keys using the active non-invasive technique attack of clock manipulation, combined with Differential Fault Analysis (DFA) cryptanalysis. The attack system is able to inject effective transient faults into the Trivium in a clock cycle and sample the faulty output. Then, the internal state of the Trivium is recovered using the DFA cryptanalysis through the comparison between the correct and the faulty outputs. Finally, a backward version of Trivium was also designed to go back and get the secret keys from the initial internal states. The key recovery has been verified with numerous simulations data attacks and used with the experimental data obtained from the Application Specific Integrated Circuit (ASIC) Trivium. The secret key of the Trivium were recovered experimentally in 100% of the attempts, considering a real scenario and minimum assumptions.

Induction Motor Multi-Fault Analysis Based on Intrinsic Mode Functions in Hilbert-Huang Transform

2009 ◽  
Author(s):  
Xin Xue ◽  
V. Sundararajan
Keyword(s):  
Induction Motor ◽  
Experimental Studies ◽  
Current Data ◽  
Fault Analysis ◽  
Vibration Sensor ◽  
Intrinsic Mode Functions ◽  
Experimental Conditions ◽  
Classification Rate ◽  
Multiple Faults ◽  
Hilbert Huang Transform

This paper reports experimental studies to detect two faults in a 3-phase 1.5hp induction motor using intrinsic mode functions from Hilbert-Huang transform. The faults studied are the eccentricity of the air-gap between the rotor and stator and damage to the outer race of bearings. The experiments are conducted under four conditions: the normal no-fault condition, two single fault conditions and the multiple faults condition. Two microphones, one vibration sensor and one current sensor are used to collect sound, vibration and current data respectively. The data is analyzed using the Hilbert-Huang transform and Fast Fourier Transform. Features are extracted from the spectrum of intrinsic mode functions and the average value of their envelope. Three simple classifiers are used to classify these four experimental conditions. The results demonstrate that the multiple sensors do improve the classification rate and that the Intrinsic Mode Functions obtained by the Hilbert-Huang transform are more effective than FFT in classifying multiple faults.

scholarly journals Dependency Model-Based Multiple Fault Diagnosis Using Knowledge of Test Result and Fault Prior Probability

2019 ◽  
Vol 9 (2) ◽  
pp. 311 ◽  
Author(s):  
Xiaofeng Lv ◽  
Deyun Zhou ◽  
Ling Ma ◽  
Yongchuan Tang
Keyword(s):  
Fault Diagnosis ◽  
Prior Probability ◽  
Test Results ◽  
Multiple Fault ◽  
Dependency Matrix ◽  
Multiple Faults ◽  
Fault Type ◽  
Multiple Fault Diagnosis ◽  
Fault Dependency ◽  
Dependency Model

Aiming at solving the multiple fault diagnosis problem as well as the sequence of all the potential multiple faults simultaneously, a new multiple fault diagnosis method based on the dependency model method as well as the knowledge in test results and the prior probability of each fault type is proposed. Firstly, the dependency model of the system can be built and used to formulate the fault-test dependency matrix. Then, the dependency matrix is simplified according to the knowledge in the test results of the system. After that, the logic ‘OR’ operation is performed on the feature vectors of the fault status in the simplified dependency matrix to formulate the multiple fault dependency matrix. Finally, fault diagnosis is based on the multiple fault dependency matrix and the ranking of each fault type calculated basing on the prior probability of each fault status. An illustrative numerical example and a case study are presented to verify the effectiveness and superiority of the proposed method.

scholarly journals An Automated Framework for Exploitable Fault Identification in Block Ciphers – A Data Mining Approach

10.29007/fmzl ◽  
2018 ◽  
Author(s):  
Sayandeep Saha ◽  
Ujjawal Kumar ◽  
Debdeep Mukhopadhyay ◽  
Pallab Dasgupta
Keyword(s):  
Data Mining ◽  
Complexity Analysis ◽  
Practical Interest ◽  
Block Ciphers ◽  
Fault Analysis ◽  
Fault Attacks ◽  
Differential Fault Analysis ◽  
Analysis Scheme ◽  
Fault Characterization

Characterization of all possible faults in a cryptosystem exploitable for fault attacks is a problem which is of both theoretical and practical interest for the cryptographic community. The complete knowledge of exploitable fault space is desirable while designing optimal countermeasures for any given crypto-implementation. In this paper, we address the exploitable fault characterization problem in the context of Differential Fault Analysis (DFA) attacks on block ciphers. The formidable size of the fault spaces demands an automated albeit fast mechanism for verifying each individual fault instance and neither thetraditional, cipher-specific, manual DFA techniques nor the generic and automated Algebraic Fault Attacks (AFA) [10] fulfill these criteria. Further, the diversified structures of different block ciphers suggest that such an automation should be equally applicable to any block cipher. This work presents an automatedframework for DFA identification, fulfilling all aforementioned criteria, which, instead of performing the attack just estimates the attack complexity for each individual fault instance. A generic and extendable data-mining assisted dynamic analysis framework capable of capturing a large class of DFA distinguishersis devised, along with a graph-based complexity analysis scheme. The framework significantly outperforms another recently proposed one [6], in terms of attack class coverage and automation effort. Experimental evaluation on AES and PRESENT establishes the effectiveness of the proposed framework in detectingmost of the known DFAs, which eventually enables the characterization of the exploitable fault space.

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