Shilling Attack Detection Using Rated Item Correlation for Collaborative Filtering

Abstract

Collaborative filtering (CF) is vulnerable under shilling attack, which misleads recommendation of CF by injecting well-crafted profiles to a targeted system. Although a number of supervised learning based shilling attack detection methods are proposed, their features mainly measure rating values and items of a profile individually, but ignore the relation between items. This study aims to enhance the robustness of CF against shilling attack by considering the rated item correlation. Real users rate items based on their preferences, but rated items are randomly selected for malicious users profiles in most shilling attack. Therefore, the rated item correlation of real and malicious profiles is different. Three features are proposed to capture the information from different intervals of the distribution of rated item correlation in terms of Cosine Association (CA). A benchmark dataset, MovieLens 100K, is used to evaluate the proposed features. The discrimination ability of the proposed features is also illustrated. The experimental results suggest that the proposed features have significant contribution on shilling attack detection.

Lab Members

Patrick Chan

Associate Professor, Vice Dean

Patrick Chan works on machine learning, deep learning, image processing, adversarial learning, and secure machine learning.