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arXiv 1303.2017cs.CRcs.NE
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基于神经网络的软件设计安全性评估

Security Assessment of Software Design using Neural Network

A. Adebiyi, Johnnes Arreymbi, Chris Imafidon

摘要:

今天,软件应用程序中的安全缺陷主要归因于设计缺陷。由于将软件发布到市场上的预算和时间有限,许多开发人员经常将安全性视为事后的考虑。以前的研究表明,在软件开发生命周期(SDLC)的后期阶段将安全性集成到软件应用程序中,其成本高于在早期阶段集成时的成本。为了帮助集成的安全早期的SDLC阶段,一种新的方法来评估安全性在设计阶段的神经网络本文研究。我们的研究结果表明,通过训练一个反向传播神经网络来识别攻击模式,可能的攻击可以识别从设计方案给它。本文的神经网络的性能的结果。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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