  In today's rapidly evolving technological landscape and advanced software
development, the rise in cyber security attacks has become a pressing concern.
The integration of robust cyber security defenses has become essential across
all phases of software development. It holds particular significance in
identifying critical cyber security vulnerabilities at the initial stages of
the software development life cycle, notably during the requirement phase.
Through the utilization of cyber security repositories like The Common Attack
Pattern Enumeration and Classification (CAPEC) from MITRE and the Common
Vulnerabilities and Exposures (CVE) databases, attempts have been made to
leverage topic modeling and machine learning for the detection of these
early-stage vulnerabilities in the software requirements process. Past research
themes have returned successful outcomes in attempting to automate
vulnerability identification for software developers, employing a mixture of
unsupervised machine learning methodologies such as LDA and topic modeling.
Looking ahead, in our pursuit to improve automation and establish connections
between software requirements and vulnerabilities, our strategy entails
adopting a variety of supervised machine learning techniques. This array
encompasses Support Vector Machines (SVM), Na\"ive Bayes, random forest, neural
networking and eventually transitioning into deep learning for our
investigation. In the face of the escalating complexity of cyber security, the
question of whether machine learning can enhance the identification of
vulnerabilities in diverse software development scenarios is a paramount
consideration, offering crucial assistance to software developers in developing
secure software.
