Clustering and Analysis of Vulnerabilities Present in Different Robot Types

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.