  Due to the new advancements in automation using Artificial Intelligence,
Robotics and Internet of Things it has become crucial to pay attention to
possible vulnerabilities in order to avoid cyber attack and hijacking that can
occur which can be catastrophic. There have been many consequences of disasters
due to vulnerabilities in Robotics, these vulnerabilities need to be analyzed
to target the severe ones before they cause cataclysm. This paper aims to
highlight the areas and severity of each type of vulnerability by analyzing
issues categorized under the type of vulnerability. This we achieve by careful
analysis of the data and application of information retrieval techniques like
Term Frequency - Inverse Document Frequency, dimension reduction techniques
like Principal Component Analysis and Clustering using Machine Learning
techniques like K-means. By performing this analysis, the severity of robotic
issues in different domains and the severity of the issue based on type of
issue is detected.
