Item Anomaly Detection Based on Dynamic Partition for Time Series in Recommender Systems.
In recent years, recommender systems have become an effective method to process information overload. However, recommendation technology still suffers from many problems. One of the problems is shilling attacks-attackers inject spam user profiles to disturb the list of recommendation items. There are two characteristics of all types of shilling attacks: 1) Item abnormality: The rating of target items is always maximum or minimum; and 2) Attack promptness: It takes only a very short period time to inject attack profiles. Some papers have proposed item anomaly detection methods based on these two characteristics, but their detection rate, false alarm rate, and universality need to be further improved. To solve these problems, this paper proposes an item anomaly detection method based on dynamic partitioning for time series. This method first dynamically partitions item-rating time series based on important points. Then, we use chi square distribution (χ2) to detect abnormal intervals. The experimental results on MovieLens 100K and 1M indicate that this approach has a high detection rate and a low false alarm rate and is stable toward different attack models and filler sizes.
Key Laboratory of Dependable Service Computing in Cyber Physical Society, Ministry of Education, Chongqing, 400044, China; School of Software Engineering, Chongqing University, Chongqing, 400044, China.
Min
M
Gao
Key Laboratory of Dependable Service Computing in Cyber Physical Society, Ministry of Education, Chongqing, 400044, China; School of Software Engineering, Chongqing University, Chongqing, 400044, China.
Renli
R
Tian
Key Laboratory of Dependable Service Computing in Cyber Physical Society, Ministry of Education, Chongqing, 400044, China; School of Software Engineering, Chongqing University, Chongqing, 400044, China.
Junhao
J
Wen
Key Laboratory of Dependable Service Computing in Cyber Physical Society, Ministry of Education, Chongqing, 400044, China; School of Software Engineering, Chongqing University, Chongqing, 400044, China.
Qingyu
Q
Xiong
School of Engineering, University of Portsmouth, Portsmouth, PO1 3AH, United Kingdom.
Bin
B
Ling
School of Engineering, University of Portsmouth, Portsmouth, PO1 3AH, United Kingdom.
Linda
L
Yang
D000465:Algorithms; D016494:Computer Security; D001330:Electronic Data Processing / Q000379:methods*