Original scientific paper

https://doi.org/10.20532/cit.2018.1003948

An Obfuscated Attack Detection Approach for Collaborative Recommender Systems

Saakshi Kapoor ; Department of Computer Science and Engineering, UIET, Panjab University, Chandigarh, India
Vishal Gupta ; Department of Computer Science and Engineering, UIET, Panjab University, Chandigarh, India
Rohit Kumar ; Department of Computer Science and Engineering, UIET, Panjab University, Chandigarh, India

Abstract

In recent times, we have loads and loads of information available over the Internet. It has become very cumbersome to extract relevant information out of this huge amount of information available. So to avoid this problem “Recommender Systems” came into play, they can predict outcomes according to user’s interests. Although Recommender Systems are very effective and useful for users but the mostly used type of Recommender System i.e. Collaborative Filtering Recommender System suffers from shilling/profile injection attacks in which fake profiles are inserted into the database in order to bias its output. With this problem in mind we propose an approach to detect attacks on Recommender Systems using Random Forest Classifier and found that when tested at 10% attack, our approach outperformed earlier proposed approaches.

Keywords

collaborative recommender systems; obfuscated attack; random forest classifier; SVM

Hrčak ID:

203982

URI

https://hrcak.srce.hr/203982

Publication date:

6.7.2018.

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