Uncover the Ghosts: Deep Learning based Spammer Detection on Social Networks
Cybercriminals have found in social networks (e.g., Twitter and Facebook) a propitious medium to launch massive cyber-attacks. Using compromised or fake accounts, criminals can exploit the inherent trust between connected users to effectively spread malicious content and perform scams against users. Detecting those malicious accounts, also known as spammer detection, on social networks has received increasing attention from government, industry, and academia. One of the long-standing unsolved challenges for spammer detection on social networks is lack of universal objective standard on feature extraction for spammer detection. Researchers often handcraft features in a subjective manner based on individual experience, which is time-consuming and inevitably induces bias to the feature extraction process. This talk introduces a new framework that provides an innovate but also practical solution to accurate and efficient spammer detection on social networks like Twitter. This framework adopts deep learning techniques that are mostly used in the area of image recognition, and develops new spammer detectors which can automatically learn universal and discriminative features to identify spammers with high accuracy.
Hao Yue received his B.E. degree in Telecommunication Engineering from Xidian University, Xi'an, China, in 2009, and Ph.D. degree in Electrical and Computer Engineering from University of Florida in 2015. He is an Assistant Professor in the Computer Science Department and the director of the Networking, Information Security, and Education (NISE) Lab at San Francisco State University. He is also the director of the Arista Academic Alliance Training program and the co-director of the OPSWAT Internship program at San Francisco State University. His research interests include Computer and Wireless Networking, Cybersecurity, and CS education. He received the best paper award from IEEE International Conference on Mobile Ad-hoc and Sensor Networks in 2017. He is the vice chair of the IEEE San Francisco Section and the chair of IEEE Communications Society San Francisco Chapter.