A Novel Kalman Filter Based Shilling Attack Detection Algorithm
This addresses security vulnerabilities in recommendation systems for users and platforms, but it is incremental as it builds on existing detection approaches with a specific method.
The paper tackles the problem of shilling attacks in collaborative filtering recommendation systems by proposing a Kalman filter-based detection model that analyzes rating differences to identify abnormal periods and suspicious users, achieving much better detection performance than traditional methods.
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.