E-Commerce recommender systems are vulnerable to different types of profile-injection
attacks where a number of fake user profiles are inserted into the system to influence the
recommendations made to the users. In this paper, we have proposed three strategies of
detecting such attacks with the help of outlier analysis. In all these strategies, the attack-
profiles are considered as outliers in the user rating dataset. Firstly, we have used Partition
around Medoid (PAM) clustering algorithm in dete cting the attack-profiles. An incremental …

As systems based on social networks grow, they get affected by huge number of fake user
profiles. Particularly, social recommender systems are vulnerable to profile injection attacks
where malicious profiles are injected into the rating system to affect user's opinion. The
objective of attackers is to inject a large set of biased profiles that provide favorable or
unfavorable recommendations for a product. In this paper, we propose a classification
technique for detection of attackers. First, we define the attributes that provide the likelihood …