METHOD AND SYSTEM FOR NEURAL NETWORK DEPLOYMENT IN SOFTWARE SECURITY VULNERABILITY TESTING

Method and system of deploying a trained machine learning neural network in dynamic testing of security vulnerability in software applications. The method comprises directing, from a security assessing server computing device, to a software program under execution, a series of attack vectors, deploying a set of results produced in accordance with the software program under execution and the attack vectors to an input layer of the trained machine learning neural network, the trained machine learning neural network comprising an output layer that is interconnected with the input layer via a set of intermediate layers, and identifying, in accordance with a predetermined threshold percentage value of false positive software security vulnerability defects, one or more software security vulnerability defects associated with the results produced, the software security vulnerability defects being generated in accordance with the output layer of the trained machine learning neural network.