On the Fault Detection Capabilities of AUTOSAR's End-to-End Communication Protection CRC's

A Fault-Resistant scheme has been proposed to secure the Advanced Encryption Standard (AES) against Differential Fault Analysis (DFA) attack. In this paper, a hybrid countermeasure has been presented in order to protect a 32-bits AES architecture proposed for resource-constrained embedded systems. A comparative study between the most well-known fault detection schemes in terms of fault detection capabilities and implementation cost has been proposed. Based on this study, we propose a hybrid fault resistant scheme to secure the AES using the parity detection for linear operations and the time redundancy for SubBytes operation. The proposed scheme is implemented on the Virtex-5 Xilinx FPGA board in order to evaluate the efficiency of the proposed fault-resistant scheme in terms of area, time costs and fault coverage (FC). Experimental results prove that the countermeasure achieves a FC with about 98,82% of the injected faults detected during the 32-bits AES process. The area overhead of the proposed countermeasure is about 14% and the additional time delay is about 13%.

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Covering Array Constructors: An Experimental Analysis of Their Interaction Coverage and Fault Detection

The Computer Journal ◽

10.1093/comjnl/bxaa020 ◽

2020 ◽

Author(s):

Rubing Huang ◽

Haibo Chen ◽

Yunan Zhou ◽

Tsong Yueh Chen ◽

Dave Towey ◽

...

Keyword(s):

Fault Detection ◽

Empirical Studies ◽

Specific Interaction ◽

Interaction Strength ◽

The Other ◽

Test Cases ◽

Covering Array ◽

Interaction Testing ◽

Detection Capabilities ◽

Combinatorial Interaction Testing

Abstract Combinatorial interaction testing (CIT) aims at constructing a covering array (CA) of all value combinations at a specific interaction strength, to detect faults that are caused by the interaction of parameters. CIT has been widely used in different applications, with many algorithms and tools having been proposed to support CA construction. To date, however, there appears to have been no studies comparing different CA constructors when only some of the CA test cases are executed. In this paper, we present an investigation of five popular CA constructors: ACTS, Jenny, PICT, CASA and TCA. We conducted empirical studies examining the five programs, focusing on interaction coverage and fault detection. The experimental results show that when there is no preference or special justification for using other CA constructors, then Jenny is recommended—because it achieves better interaction coverage and fault detection than the other four constructors in many cases. Our results also show that when using ACTS or CASA, their CAs must be prioritized before testing. The main reason for this is that these CAs can result in considerable interaction coverage or fault detection capabilities when executing a large number of test cases; however, they may also produce the lowest rates of fault detection and interaction coverage.

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Fault detection capabilities of coupling-based OO testing

13th International Symposium on Software Reliability Engineering, 2002. Proceedings. ◽

10.1109/issre.2002.1173253 ◽

2003 ◽

Cited By ~ 3

Author(s):

R.T. Alexander ◽

J. Offutt ◽

J.M. Biemann

Keyword(s):

Fault Detection ◽

Detection Capabilities

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Fault Detection Based on Fully Convolutional Networks (FCN)

Journal of Marine Science and Engineering ◽

10.3390/jmse9030259 ◽

2021 ◽

Vol 9 (3) ◽

pp. 259

Author(s):

Jizhong Wu ◽

Bo Liu ◽

Hao Zhang ◽

Shumei He ◽

Qianqian Yang

Keyword(s):

Fault Detection ◽

Seismic Data ◽

Binary Image ◽

Training Data ◽

Real Field ◽

Convolutional Network ◽

Efficient Manner ◽

Data Set ◽

Seismic Image ◽

End To End

It is of great significance to detect faults correctly in continental sandstone reservoirs in the east of China to understand the distribution of remaining structural reservoirs for more efficient development operation. However, the majority of the faults is characterized by small displacements and unclear components, which makes it hard to recognize them in seismic data via traditional methods. We consider fault detection as an end-to-end binary image-segmentation problem of labeling a 3D seismic image with ones as faults and zeros elsewhere. Thus, we developed a fully convolutional network (FCN) based method to fault segmentation and used the synthetic seismic data to generate an accurate and sufficient training data set. The architecture of FCN is a modified version of the VGGNet (A convolutional neural network was named by Visual Geometry Group). Transforming fully connected layers into convolution layers enables a classification net to create a heatmap. Adding the deconvolution layers produces an efficient network for end-to-end dense learning. Herein, we took advantage of the fact that a fault binary image is highly biased with mostly zeros but only very limited ones on the faults. A balanced crossentropy loss function was defined to adjust the imbalance for optimizing the parameters of our FCN model. Ultimately, the FCN model was applied on real field data to propose that our FCN model can outperform conventional methods in fault predictions from seismic images in a more accurate and efficient manner.

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End-To-End Unsupervised Fault Detection Using A Flow-Based Model

Reliability Engineering & System Safety ◽

10.1016/j.ress.2021.107805 ◽

2021 ◽

pp. 107805

Author(s):

Liangwei Zhang ◽

Jing Lin ◽

Haidong Shao ◽

Zhicong Zhang ◽

Xiaohui Yan ◽

...

Keyword(s):

Fault Detection ◽

End To End

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