A Novel Kalman Filter Based Shilling Attack Detection Algorithm
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Collaborative FilteringRecommendation SystemsSimilar Papers 제목 키워드 기반
Advancing Recommender Systems by mitigating Shilling attacks
Considering the premise that the number of products offered grow in an exponential fashion and the amount of data that a user can assimilate before making a decision is relatively small, recommender systems help in categ…
Collaborative FilteringRecommendation SystemsDetecting Abnormal Profiles in Collaborative Filtering Recommender Systems
Personalization collaborative filtering recommender systems (CFRSs) are the crucial components of popular e-commerce services. In practice, CFRSs are also particularly vulnerable to "shilling" attacks or "profile injecti…
Collaborative FilteringRecommendation SystemsDefending Grey Attacks by Exploiting Wavelet Analysis in Collaborative Filtering Recommender Systems
"Shilling" attacks or "profile injection" attacks have always major challenges in collaborative filtering recommender systems (CFRSs). Many efforts have been devoted to improve collaborative filtering techniques which ca…
Collaborative FilteringRecommendation SystemsDetection of Shilling Attack Based on T-distribution on the Dynamic Time Intervals in Recommendation Systems
With the development of information technology and the Internet, recommendation systems have become an important means to solve the problem of information overload. However, recommendation system is greatly fragile as it…
Anomaly DetectionRecommendation SystemsSingle-User Injection for Invisible Shilling Attack against Recommender Systems
Recommendation systems (RS) are crucial for alleviating the information overload problem. Due to its pivotal role in guiding users to make decisions, unscrupulous parties are lured to launch attacks against RS to affect …
Recommendation Systems