An Obfuscated Attack Detection Approach for Collaborative Recommender Systems

Sa？etak 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