Detecting unfair ratings attacks in online rating systems

• Main Author: Ho, Wenxu.
• Other Authors: School of Computer Engineering
• Format: Final Year Project
• Language: English
• Published: 2013
• Subjects: DRNTU::Engineering::Computer science and engineering::Computing methodologies::Simulation and modeling
• Online Access: http://hdl.handle.net/10356/51993
• Institution: Nanyang Technological University

E-commerce or Electronic commerce has become part and parcel of everyone’s daily life as it provides people with a platform for buying and selling products or service over the usage of an electronic system such as the Internet. It has become a convenient tool for people to find out information on the various products or services offered at the comfort of their homes. In order to ensure of getting the “best deals”, most people tend to turn to online rating systems for advices to make informed decisions regarding the purchase of products or services. However, it remains a mystery on how reliable or trustworthy are such rating systems. Often, these rating systems are susceptible to malicious attacks which mislead the consumers. This report aims to evaluate the effectiveness of 2 defence models namely the Bayesian Reputation System (BRS) and the Integrated Clustering Based Approach known as iClub against common sighted attacks such as Constant, Camouflage, Sybil, Whitewashing and various combined attacks over several key performance metrics.