Shuo Qiu, Student Member, IEEE, Boyang Wang, Ming Li, Member, IEEE, Jiqiang Liu, and Yanfeng Shi, “Toward Practical Privacy-Preserving Frequent Itemset Mining on Encrypted Cloud Data”, IEEE Transactions on Cloud Computing, 2020.

Domain – Java Project – Data Science – Data Mining

Fake reviews and ratings becomes annoying forever in the user perspective and in the field of consumer utilization. Some users crate and inject fake user profiles consisting of biased ratings which affects the recommendation ranking and manipulate the user’s decision. Attacks on recommender system behaviour is known as a “shilling” attack or “profile injection” attack. The fake Users involved in shilling attacks were coined as shillers. Existing shilling attack detection doesn’t have a clear approach 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 project, group shilling attack detection can be achieved based on bisecting K-means clustering algorithm. Time based manipulation is the base idea – 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. Experiments on the Netflix and Amazon data sets with the algorithm implementation indicate that the proposed method outperforms the baseline methods.”
• System : Pentium i3 Processor
• Hard Disk : 500 GB.
• Monitor : 15’’ LED
• Input Devices : Keyboard, Mouse
• Ram : 2 GB
• Operating system : Windows 10.
• Coding Language : Java
• Tool : Netbeans 8.2
• Database : MYSQL