Xiaolong Deng

Beijing University of Posts and Telecommunications

Papers

Total Citations

H-Index

About

Xiaolong Deng is a researcher whose work lies at the intersection of machine learning, social robotics, and cybersecurity. His most cited paper, "Variational Autoencoder Based Enhanced Behavior Characteristics Classification for Social Robot Detection" (2020, 4 citations), introduces a novel approach to distinguishing between human and automated social media accounts. By leveraging variational autoencoders, Deng enhances the classification of behavioral characteristics, offering a more robust method for detecting social bots—a critical challenge in the age of digital misinformation. This contribution not only advances the technical capabilities of bot detection but also underscores the importance of integrating generative models into cybersecurity frameworks. Deng’s research is particularly impactful for students and researchers exploring the convergence of AI and social network analysis, as it provides a scalable solution to a pressing real-world problem. His work demonstrates a keen ability to apply complex machine learning techniques to practical security issues, marking him as a thoughtful contributor to the field. With a focus on behavior-based classification, Deng continues to shape how we understand and safeguard online interactions.