Detection of Compromised Accounts using Machine Learning Based Boosting Algorithms- AdaBoost, XGBoost, and CatBoost
Arti Pandey, Arti Jain · 2023
In today’s digital era, Online Social Media (OSM) like Twitter, Facebook, and Instagram are used to share information. Everyone is trusted on these social network platforms for exchanging information such as personal, professional, financial, etc. Due to this implicit faith in these social platforms, users broadcast their personal information on them. By taking advantage of the same, unauthorized users use the account of legitimate users for performing malicious activities. They gain valuable information and legitimate accounts are trapped in malicious hands. These types of accounts are known as Compromised Accounts. In order to create a safe and secure social media environment, it is, therefore, essential to identify these compromised accounts. In this paper, for the detection of Compromised Accounts, we have applied machine learning-based three boosting algorithms- AdaBoost, XGBoost, and CatBoost. These algorithms are compared with the state-of-art Compromised Account Detection using the Authorship Verification (CAD-AV) approach over the real-world Twitter dataset. The results have shown that all three algorithms- AdaBoost, XGBoost, and CatBoost have performed better in comparison to CAD-AV. Among them, CatBoost has performed the best (Accuracy: 94%) in the detection of compromised accounts.