Detecting unfair ratings attacks in online rating systems
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 th...
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| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
2013
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| Online Access: | http://hdl.handle.net/10356/51993 |
| _version_ | 1824453200509403136 |
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| author | Ho, Wenxu. |
| author2 | School of Computer Engineering |
| author_facet | School of Computer Engineering Ho, Wenxu. |
| author_sort | Ho, Wenxu. |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T07:56:37Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/51993 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:56:37Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/519932023-03-03T20:57:32Z Detecting unfair ratings attacks in online rating systems Ho, Wenxu. School of Computer Engineering Zhang Jie DRNTU::Engineering::Computer science and engineering::Computing methodologies::Simulation and modeling 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. Bachelor of Engineering (Computer Engineering) 2013-04-19T02:55:51Z 2013-04-19T02:55:51Z 2013 2013 Final Year Project (FYP) http://hdl.handle.net/10356/51993 en Nanyang Technological University 43 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Computer science and engineering::Computing methodologies::Simulation and modeling Ho, Wenxu. Detecting unfair ratings attacks in online rating systems |
| title | Detecting unfair ratings attacks in online rating systems |
| title_full | Detecting unfair ratings attacks in online rating systems |
| title_fullStr | Detecting unfair ratings attacks in online rating systems |
| title_full_unstemmed | Detecting unfair ratings attacks in online rating systems |
| title_short | Detecting unfair ratings attacks in online rating systems |
| title_sort | detecting unfair ratings attacks in online rating systems |
| topic | DRNTU::Engineering::Computer science and engineering::Computing methodologies::Simulation and modeling |
| url | http://hdl.handle.net/10356/51993 |
| work_keys_str_mv | AT howenxu detectingunfairratingsattacksinonlineratingsystems |