Abstract
The shilling attack detection methods or approaches that are previously proposed keep emphasis on identifying the shillers or the attackers that act individually. There is a lack of focus on the attackers or shillers that act as a group. A shilling attack is an attack in which fake profiles are injected. When such profiles are present in an online recommendation system, the output that is generated by the online recommendation system will be biased. This results in inaccuracy of the online recommendation system. Here, we are proposing a shilling attack detection system in which we can not only identify individual shillers but also group shillers. In this system, the candidate groups are generated with an interval of time and are then grouped according to a degree called group suspicion degree. Then by making use of the DBSCAN algorithm and clustering of the groups which are suspicious is done. These groups are the shilling groups.
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