Mitigating Software Security Flaws with Automation

A group of UTSA researchers is investigating how a new automated approach could be used to prevent software security vulnerabilities. The team intended to create a deep learning model that could train the software on how to automatically extract security policies. 

Unlike traditional software development models, the agile software development process is intended to deliver software more quickly, eradicating the requirement for lengthy paperwork and changing software requirements. The only required documentation is user stories, which are specifications that define the software’s requirements. However, the fundamental practises of this method, such as frequent code changes, restrict the capacity to perform security assurance evaluations.

Ram Krishnan, associate professor in the UTSA Department of Electrical and Computer Engineering stated, “The basic idea of addressing this disconnect between security policies and agile software development came from happenstance conversation with software leaders in the industry.” 

Before arriving on a deep learning strategy that can handle several formats of user stories, the researchers looked at various machine learning approaches. To conduct the prediction, the model is composed of three parts: access control classifications, named entity recognition, and access type classification. The software uses access control classification to determine whether or not user stories contain access control information. The actors and data objects in the storey are identified by a na

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