  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.
