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Reducing model complexity and cost in the generation of efficient error detection mechanisms
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Leeke, Matthew (2021) Reducing model complexity and cost in the generation of efficient error detection mechanisms. In: 19th IEEE International Conference on Dependable, Autonomic and Secure Computing (DASC'21), Calgary, Canada, 25-28 Oct 2021. Published in: Proceedings of the 19th IEEE International Conference on Dependable, Autonomic and Secure Computing (DASC'21) pp. 26-34.
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WRAP-Reducing-model-complexity-cost-generation-efficient-error-detection-mechanisms-2021.pdf - Accepted Version - Requires a PDF viewer. Download (435Kb) | Preview |
Official URL: http://cyber-science.org/2021/dasc/
Abstract
The design and location of error detection mechanisms (EDMs) is fundamental to the design of a dependable software system. The application of machine learning algorithms to fault injection data has been shown to be an effective approach for the generation of efficient EDMs. However, the complexity of the generated models and initial cost of generation represent barriers to the adoption of the approach. Addressing these challenges directly, this paper demonstrates that genetic programming can be used as an approach to reduce the complexity of the models generated and obviate the computational cost associated with the sampling and refinement stages of EDM generation. More specifically, it is shown that (i) genetic programming can be used to project the instance space of fault injection data sets into a space more amenable to learning, (ii) machine learning algorithms can be applied to the resultant projection to permit the generation of efficient EDMs with reduced model complexity, and (iii) the cost of generating efficient EDMs can be reduced by the approach because it obviates the need for data set sampling methods and model refinement.
Item Type: | Conference Item (Paper) | ||||||
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Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software | ||||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||
Library of Congress Subject Headings (LCSH): | Error-correcting codes (Information theory), Algorithms, Fault-tolerant computing, Computer Communication Networks, Computational complexity | ||||||
Journal or Publication Title: | Proceedings of the 19th IEEE International Conference on Dependable, Autonomic and Secure Computing (DASC'21) | ||||||
Publisher: | IEEE | ||||||
Official Date: | 25 October 2021 | ||||||
Dates: |
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Page Range: | pp. 26-34 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Reuse Statement (publisher, data, author rights): | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||
Copyright Holders: | IEEE | ||||||
Date of first compliant deposit: | 4 November 2021 | ||||||
Date of first compliant Open Access: | 4 November 2021 | ||||||
Conference Paper Type: | Paper | ||||||
Title of Event: | 19th IEEE International Conference on Dependable, Autonomic and Secure Computing (DASC'21) | ||||||
Type of Event: | Conference | ||||||
Location of Event: | Calgary, Canada | ||||||
Date(s) of Event: | 25-28 Oct 2021 | ||||||
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