NDLI: Detection of Shilling Attacks in Collaborative Recommender Systems

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Artificial Intelligence (AI)

Communication Systems (ECC)

Computer Integrated Manufacturing (CIM)

Computer Science (CS)

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Industrial Instrumentation and Control (IIC)

Industrial Refrigeration and Cryogenic Engineering (IRCE)

Master of Computer Applications (MCA)

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Transportation Engineering (TE)

Urban Planning (UP)

Detection of Shilling Attacks in Collaborative Recommender Systems

Content Provider	TKM College of Engineering
Advisor	Raju, Jini
Author	Raju, Siby
Abstract	Recommender systems are backbone for ecommerce website today. With the rapid growth in the ecommerce web sites, recommendation systems plays very important role to provide personalized service to the user. A good recommender system determines the quality of service provided by ecommerce. Ecommerce websites like amazon.com and ebay.com are widely popular because of their recommender systems. Collaborative filtering is one of the most widely used recommendation system. Recommendations made using collaborative filtering depend on relationship between the user and items. Unfortunately, due to its openness and dependency on user ratings, Collaborative filtering is prone to shilling attack and problem is with their security. Such attacks alter the recommendation process to promote or demote a particular product. Attacker who cannot be separated with distinguished from genuine user may inject biased profiles in the system to affect the service of system. It may leads to degradation of recommender system’s objective. It is therefore essential to detect the shilling attacks in such a way that there are in-depth analysis of user behaviors and uses two key mechanisms (i.e., behavior features extraction and detection) to distinguish shilling profiles from genuine ones. In the stage of detection, a classifier is then built to distinguish attack profiles from genuine user profiles by constructing training data from authentic profiles and attacks generated by attack models. The combined effectiveness of this approach is then evaluated with the supervised classification algorithm Support Vector Machine. The experimental results demonstrate that proposed supervised detection model achieve a better detection performance is about 85.41% in shilling detection.
Language	English
Publisher Date	2022-09-01
Access Restriction	Open
Subject Keyword	Principle Component Analysis Collaborative Filtering Support Vector Machine (SVM) K-Nearest Neighbour
Content Type	Text
Resource Type	Thesis

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