Title

Detection Of Profile Injection Attacks In Social Recommender Systems Using Outlier Analysis

Abstract

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 of a user having a profile of that of an attacker. Using user-item rating matrix, user-connection matrix, and similarity between users, we find if the ratings are abnormal and if there are random connections in the network. Then, we use fc-means clustering to categorize users into authentic users and attackers. To evaluate our framework, we use Epinions dataset and inject intelligent push and nuke attacks. These attacks make arbitrary connections to existing users and provide biased ratings. To evaluate the performance, we use precision and recall to show that fc-means clustering can identify the attackers with high accuracy and low false positives.

Publication Date

7-1-2017

Publication Title

Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017

Volume

2018-January

Number of Pages

2714-2719

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/BigData.2017.8258235

Socpus ID

85047762069 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85047762069

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