Integrated Geophysical Investigation and 3-D Fault Characterization of the Rochester and Adna 7.5 Minute Quadrangles, Thurston and Lewis Counties, Washington

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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Support vector machines for detection and characterization of rolling element bearing faults

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/095440620121500907 ◽

2001 ◽

Vol 215 (9) ◽

pp. 1065-1074 ◽

Cited By ~ 63

Author(s):

L. B. Jack ◽

A. K. Nandi

Keyword(s):

Support Vector Machines ◽

Vibration Signal ◽

Training Data ◽

Rolling Element Bearing ◽

Support Vector ◽

Statistical Estimates ◽

Vector Machines ◽

Rolling Element ◽

Fault Characterization

Artificial neural networks (ANNs) have been used to detect faults in rotating machinery for a number of years, using statistical estimates of the vibration signal as input features, and they have been shown to be highly successful in this type of application. Support vector machines (SVMs) are a more recent development, and little use has been made of them in the condition monitoring (CM) arena. The availability of a limited amount of training data creates some problems for the use of SVMs, and a strategy is offered that improves the generalization performance significantly in cases where only limited training data are available. This paper examines the performance of both types of classifier in one given scenario—a multiclass fault characterization example.

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