Profile Verification and Secured Social Engineering with Machine Learning Models

Abstract Digital media as a platform is one of highest interactive yet sensitive arenas of information technology. With the rise of Online Social Networks, the idea of authentic identity is often seen on the brink of attack with the impact of cases of Social Engineering and other forms of Cyber Threats. The research here mainly aims at developing a Profile Verification Model with existing datasets from platforms such as Instagram. This includes understanding the behavioural aspects of interactions with masked identity elements such as bots and fake accounts Using the concepts of Supervised Machine Learning, its objective is to use existing algorithms such as Random Forest Classifier, K-Nearest Neighbour Classifier, and Linear SVM, using data pre-processing techniques to transform valid input training and testing formats. Finally, drawing comparative results using mathematical operations for accuracy and testing and depict the same through data visualization tools.