Applicability of Neural Networks to Software Security

Abstract

Software design flaws account for 50% software security vulnerability today. As attacks on vulnerable software continue to increase, the demand for secure software is also increasing thereby putting software developers under more pressure. This is especially true for those developers whose primary aim is to produce their software quickly under tight deadlines in order to release it into the market early. While there are many tools focusing on implementation problems during software development lifecycle (SDLC), this does not provide a complete solution in resolving software security problems. Therefore designing software with security in mind will go a long way in developing secure software. In this paper some of the current approaches used in integrating security at the design level of SDLC are discussed briefly and a new method of evaluating software design using neural network is presented. With the aid of the proposed neural network tool, this research found out that software design scenarios can be matched to attack patterns that identify the security flaws in the design scenarios. The result of performance of the neural network is presented in this paper.