Detecting Malicious Users and Remove Rumors from Social Network

Rumors and Social Networks

Why do people spread rumors? This paper studies the transmission of possibly false information—by rational agents who seek the truth. Unbiased agents earn payoffs when a collective decision is correct in that it matches the true state of the world. Biased agents desire a particular decision regardless of the true state. One agent possibly learns the true underlying state and chooses whether to ...

A Malicious Users Detecting Model Based on Feedback Correlations

The trust and reputation models were introduced to restrain the impacts caused by rational but selfish peers in P2P streaming systems. However, these models face with two major challenges from dishonest feedback and strategic altering behaviors. To answer these challenges, we present a global trust model based on network community, evaluation correlations, and punishment mechanism. We also prop...

Detecting Rumors from Microblogs with Recurrent Neural Networks

Jing Ma, Wei Gao, Prasenjit Mitra, Sejeong Kwon, Bernard J. Jansen, Kam-Fai Wong, Meeyoung Cha The Chinese University of Hong Kong, Hong Kong SAR Qatar Computing Research Institute, Hamad Bin Khalifa University, Qatar Graduate School of Culture Technology, Korea Advanced Institute of Science and Technology, Korea 1{majing,kfwong}@se.cuhk.edu.hk, 2{wgao,pmitra,bjansen}@qf.org.qa, 3{gsj1029,meeyo...

Study on Network-Based Technology for Detecting Potentially Malicious Websites

An FPGA System for Detecting Malicious DNS Network Traffic

Billions of legitimate packets traverse computer networks every day. Unfortunately, malicious traffic also traverses these same networks. An example is traffic that abuses the Domain Name System (DNS) protocol to exfiltrate sensitive data, establish backdoor tunnels or control botnets. This paper describes the TRAPP-2 system, an extended version of the Tracking and Analysis for Peer-to-Peer (TR...