Detection of shilling attack in recommender system for YouTube video statistics using machine learning techniques

Literature survey shows that the recommendation systems have been largely adapted and evaluated in various domains. Due to low performances from various cyber attacks, the adoption of recommender system is in the initial stage of defense systems. One of the most common attacks for recommender system is shilling attack. There are some existing techniques for identifying the shilling attacks built in the user ratings patterns. The performance of ratings on target items differs between the attack user profiles and actual user profiles. To differentiate the certain profiles, the affected profiles are known as attack profiles. Besides the shilling attacks, real cyber attacks are taking place in the community which are being solved by Petri Net methods. These attacks can be falsely predicted (shilling attacks) by the users which can raise security threats. For identifying various shilling attacks without a priori knowledge, Recommendation System suffers from low accuracy. Basically, recommendation attack is split into nuke and push attack that encourage and discourage the recommended target item. The strength of shilling attack is usually measured by filler size and attack size. An experiment over unsupervised machine learning algorithms with filler size 3% over 3%, 5%, 8% and 10% attack sizes is presented for Netflix dataset. Furthermore, we conducted an experiment on data of 26 K videos on the Trending YouTube Video Statistics, to predict the user preferences for a particular genre of videos using Machine Learning Algorithms. Based on the results, it observed that the Boosted Decision tree performs the best with an accuracy of 99 percent.