Patent ID: 8839441

Claim:
A computer implemented method for adaptively training a scanner offline using a trained neural network for scanning vulnerability in a software application, the method comprising: converting at least one software application into binary format data; providing the binary format data of the at least one software application as an input to the trained neural network; scanning the binary format data of the at least one software application using the trained neural network for identifying at least one vulnerability by executing a predefined set of rules for the scanner, each of the predefined set of rules being represented as a different node of the trained neural network, wherein the predefined set of rules is derived based on a predefined set of vulnerabilities associated with one or more software applications; analyzing the identified at least one vulnerability of the at least one software application presented after the scanning, wherein the analyzing comprises comparing the identified at least one vulnerability of the at least one software application with an original set of vulnerabilities associated with the at least one software application; modifying the predefined set of rules by adjusting one or more weights of nodes of the trained neural network if the identified at least one vulnerability of the at least one software application does not match with the original set of vulnerabilities associated with the at least one software application; repeating the steps of scanning, analyzing, and modifying until the identified at least one vulnerability in the at least one software application match with the original set of vulnerabilities; and training the scanner with the modified predefined set of rules for scanning another software application to identify one or more vulnerabilities after the at least one vulnerability is matched with the original set of vulnerabilities.