Detecting Group Shilling Attacks in Online Recommender Systems Based on Bisecting K-Means Clustering
Detecting Group Shilling Attacks in Online Recommender Systems Based on Bisecting K-Means Clustering
IEEE BASE PAPER ABSTRACT:
Existing shilling attack detection approaches focus mainly on identifying individual attackers in online recommender systems and rarely address the detection of group shilling attacks in which a group of attackers colludes to bias the output of an online recommender system by injecting fake profiles. In this article, we propose a group shilling attack detection method based on the bisecting K-means clustering algorithm. First, we extract the rating track of each item and divide the rating tracks to generate candidate groups according to a fixed time interval. Second, we propose item attention degree and user activity to calculate the suspicious degrees of candidate groups. Finally, we employ the bisecting K-means algorithm to cluster the candidate groups according to their suspicious degrees and obtain the attack groups. The results of experiments on the Netflix and Amazon data sets indicate that the proposed method outperforms the baseline methods.
PROJECT OUTPUT VIDEO:
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
- System : Pentium i3 Processor
- Hard Disk : 500 GB.
- Monitor : 15’’ LED
- Input Devices : Keyboard, Mouse
- Ram : 2 GB
SOFTWARE REQUIREMENTS:
- Operating System : Windows 10 / 11.
- Coding Language : JAVA.
- Frontend : JSP, HTML, CSS, JavaScript.
- IDE Tool : NetBeans IDE 8.2.
- Database : MYSQL.
REFERENCE:
Fuzhi Zhang and Shilei Wang, “Detecting Group Shilling Attacks in Online Recommender Systems Based on Bisecting K-Means Clustering”, IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS, 2020.
