Zhengge Dai

Beijing University of Posts and Telecommunications

Papers

Total Citations

H-Index

About

Zhengge Dai is a researcher specializing in cybersecurity and social network analysis, with a particular focus on the detection and mitigation of malicious automated accounts — commonly known as social robots — that threaten the integrity of online information ecosystems. Dai's work sits at the intersection of machine learning and network security, leveraging cutting-edge deep learning architectures to address real-world digital threats. Among Dai's most notable contributions is a 2019 study introducing a Generative Adversarial Network (GAN)-based approach to detecting malicious social robots, a method that moves beyond traditional classification techniques to more robustly identify automated accounts spreading harmful content across social platforms. This work has garnered 7 citations, reflecting its relevance in a rapidly growing field. Building on this foundation, Dai's 2020 research employed Variational Autoencoders (VAEs) to analyze and classify behavioral characteristics of social robots, further advancing the sophistication of detection methodologies. Dai's research speaks to an urgent and expanding challenge in digital society — the manipulation of online discourse through automated agents — making their contributions valuable to both the academic community and practitioners working to safeguard information security and maintain healthy online environments.