Our lab aims to improve security and privacy in online and geosocial networks. In recent years our main focus is on the automated abuse detection and defense system for online social networks like Facebook and developing spammer detection and training tools. We develop novel systems and machine learning methods that have practical applications in fraud and abuse detection in online and geosocial networks like Facebook, Twitter, Instagram, WeChat, etc. The overarching goal of our research is to design and develop machine learning-based automated defense techniques against fraud and abuse to ensure a secure and reliable online social networking experience.

We investigate different fundamental social networking security and privacy challenges. We particularly target Facebook that promotes friend relationships that can lead to numerous abuses ranging from collection and misuse of sensitive private data to cyberbullying and distribution of malware, fake news, and propaganda. Our research challenges the proposed academic work and existing implementations and introduces novel approaches to develop privacy-enhanced secure solutions.