A Novel Kalman Filter Based Shilling Attack Detection Algorithm

TL;DR

This paper introduces a Kalman filter-based algorithm to detect shilling attacks in collaborative filtering recommendation systems by analyzing rating anomalies over time.

Contribution

It proposes a novel Kalman filter approach for identifying malicious fake profiles in recommendation systems, improving detection accuracy over traditional methods.

Findings

Outperforms traditional detection methods in experiments

Effectively identifies abnormal rating patterns

Reduces false positives in attack detection

Abstract

Collaborative filtering has been widely used in recommendation systems to recommend items that users might like. However, collaborative filtering based recommendation systems are vulnerable to shilling attacks. Malicious users tend to increase or decrease the recommended frequency of target items by injecting fake profiles. In this paper, we propose a Kalman filter-based attack detection model, which statistically analyzes the difference between the actual rating and the predicted rating calculated by this model to find the potential abnormal time period. The Kalman filter filters out suspicious ratings based on the abnormal time period and identifies suspicious users based on the source of these ratings. The experimental results show that our method performs much better detection performance for the shilling attack than the traditional methods.