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DOI: 10.14569/IJARAI.2012.010401
PDF

Security Assessment of Software Design using Neural Network

Author 1: A Adebiyi
Author 2: Johnnes Arreymbi
Author 3: Chris Imafidon

International Journal of Advanced Research in Artificial Intelligence(IJARAI), Volume 1 Issue 4, 2012.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

Abstract: Security flaws in software applications today has been attributed mostly to design flaws. With limited budget and time to release software into the market, many developers often consider security as an afterthought. Previous research shows that integrating security into software applications at a later stage of software development lifecycle (SDLC) has been found to be more costly than when it is integrated during the early stages. To assist in the integration of security early in the SDLC stages, a new approach for assessing security during the design phase by neural network is investigated in this paper. Our findings show that by training a back propagation neural network to identify attack patterns, possible attacks can be identified from design scenarios presented to it. The result of performance of the neural network is presented in this paper.

Keywords: Neural Networks; Software security; Attack Patterns.

A Adebiyi, Johnnes Arreymbi and Chris Imafidon, “Security Assessment of Software Design using Neural Network” International Journal of Advanced Research in Artificial Intelligence(IJARAI), 1(4), 2012. http://dx.doi.org/10.14569/IJARAI.2012.010401

@article{Adebiyi2012,
title = {Security Assessment of Software Design using Neural Network},
journal = {International Journal of Advanced Research in Artificial Intelligence},
doi = {10.14569/IJARAI.2012.010401},
url = {http://dx.doi.org/10.14569/IJARAI.2012.010401},
year = {2012},
publisher = {The Science and Information Organization},
volume = {1},
number = {4},
author = {A Adebiyi and Johnnes Arreymbi and Chris Imafidon}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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