Using Trust and Reputation for Detecting Groups of Colluded Agents in Social Networks
One of the most common types of malicious behavior in social networks is represented by collusion, which consists of a secret cooperation between two or more agents providing mutual, highly positive feedback to each other. This collusion creates misleading advantages for the involved agents, deceivi...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10815731/ |
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| author | Mariantonia Cotronei Sofia Giuffre Attilio Marciano Domenico Rosaci Giuseppe M. L. Sarne |
| author_facet | Mariantonia Cotronei Sofia Giuffre Attilio Marciano Domenico Rosaci Giuseppe M. L. Sarne |
| author_sort | Mariantonia Cotronei |
| collection | DOAJ |
| description | One of the most common types of malicious behavior in social networks is represented by collusion, which consists of a secret cooperation between two or more agents providing mutual, highly positive feedback to each other. This collusion creates misleading advantages for the involved agents, deceiving others and distorting the actual reputation perception of the colluding members. Although the well-known EigenTrust algorithm can be fruitfully used to detect colluded agents, two important issues arise which limit its effectiveness: 1) it requires input information about which agents can be a-priori considered particularly trustworthy; and 2) it is not designed to handle situations in which we have several, different groups of colluded agents. These problems lead EigenTrust, to produce a significant number of false positives in some real situations. In this paper, we address the aforementioned issues. We introduce an automatic procedure to provide EigenTrust with the necessary inputs, and we propose an appropriate algorithm that combines EigenTrust with a clustering process. This procedure groups agents based on their reputation scores to tackle the presence of different groups of colluded agents. Through experiments, we demonstrate that our method, while maintaining the same effectiveness as EigenTrust in detecting malicious agents, is significantly more capable of avoiding the generation of false positives. |
| format | Article |
| id | doaj-art-f802bb25cf1640af99d9cc11a518bdcc |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-f802bb25cf1640af99d9cc11a518bdcc2025-01-10T00:02:16ZengIEEEIEEE Access2169-35362025-01-01131511152110.1109/ACCESS.2024.352256010815731Using Trust and Reputation for Detecting Groups of Colluded Agents in Social NetworksMariantonia Cotronei0https://orcid.org/0000-0003-4374-298XSofia Giuffre1https://orcid.org/0000-0001-8503-9630Attilio Marciano2https://orcid.org/0000-0002-7229-5464Domenico Rosaci3https://orcid.org/0000-0002-9256-9995Giuseppe M. L. Sarne4https://orcid.org/0000-0003-3753-6020Department of Information Engineering, Infrastructure and Sustainable Energy (DIIES), Mediterranea University of Reggio Calabria, Reggio Calabria, ItalyDepartment of Information Engineering, Infrastructure and Sustainable Energy (DIIES), Mediterranea University of Reggio Calabria, Reggio Calabria, ItalyDepartment of Information Engineering, Infrastructure and Sustainable Energy (DIIES), Mediterranea University of Reggio Calabria, Reggio Calabria, ItalyDepartment of Information Engineering, Infrastructure and Sustainable Energy (DIIES), Mediterranea University of Reggio Calabria, Reggio Calabria, ItalyDepartment of Psychology, University of Milano-Bicocca, Milan, ItalyOne of the most common types of malicious behavior in social networks is represented by collusion, which consists of a secret cooperation between two or more agents providing mutual, highly positive feedback to each other. This collusion creates misleading advantages for the involved agents, deceiving others and distorting the actual reputation perception of the colluding members. Although the well-known EigenTrust algorithm can be fruitfully used to detect colluded agents, two important issues arise which limit its effectiveness: 1) it requires input information about which agents can be a-priori considered particularly trustworthy; and 2) it is not designed to handle situations in which we have several, different groups of colluded agents. These problems lead EigenTrust, to produce a significant number of false positives in some real situations. In this paper, we address the aforementioned issues. We introduce an automatic procedure to provide EigenTrust with the necessary inputs, and we propose an appropriate algorithm that combines EigenTrust with a clustering process. This procedure groups agents based on their reputation scores to tackle the presence of different groups of colluded agents. Through experiments, we demonstrate that our method, while maintaining the same effectiveness as EigenTrust in detecting malicious agents, is significantly more capable of avoiding the generation of false positives.https://ieeexplore.ieee.org/document/10815731/Multi-agent systemsrecursive modelsreputationsocial networkstrust |
| spellingShingle | Mariantonia Cotronei Sofia Giuffre Attilio Marciano Domenico Rosaci Giuseppe M. L. Sarne Using Trust and Reputation for Detecting Groups of Colluded Agents in Social Networks IEEE Access Multi-agent systems recursive models reputation social networks trust |
| title | Using Trust and Reputation for Detecting Groups of Colluded Agents in Social Networks |
| title_full | Using Trust and Reputation for Detecting Groups of Colluded Agents in Social Networks |
| title_fullStr | Using Trust and Reputation for Detecting Groups of Colluded Agents in Social Networks |
| title_full_unstemmed | Using Trust and Reputation for Detecting Groups of Colluded Agents in Social Networks |
| title_short | Using Trust and Reputation for Detecting Groups of Colluded Agents in Social Networks |
| title_sort | using trust and reputation for detecting groups of colluded agents in social networks |
| topic | Multi-agent systems recursive models reputation social networks trust |
| url | https://ieeexplore.ieee.org/document/10815731/ |
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