A novel Kalman Filter based shilling attack detection algorithm

Xin Liu; Yingyuan Xiao; Xu Jiao; Wenguang Zheng; Zihao Ling; Xin Liu; Yingyuan Xiao; Xu Jiao; Wenguang Zheng; Zihao Ling

doi:10.3934/mbe.2020081

Mathematical Biosciences and Engineering

2020, Volume 17, Issue 2: 1558-1577. doi: 10.3934/mbe.2020081

Previous Article Next Article

Research article Special Issues

A novel Kalman Filter based shilling attack detection algorithm

Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin University of Technology, Tianjin 300384, China;Key Laboratory of Computer Vision and System, Ministry of Education, Tianjin University of Technology, Tianjin 300384, China

Received: 12 September 2019 Accepted: 01 December 2019 Published: 04 December 2019

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.
- collaborative filtering,
- recommendation system,
- shilling attack,
- attack detection,
- Kalman Filter
Citation: Xin Liu, Yingyuan Xiao, Xu Jiao, Wenguang Zheng, Zihao Ling. A novel Kalman Filter based shilling attack detection algorithm[J]. Mathematical Biosciences and Engineering, 2020, 17(2): 1558-1577. doi: 10.3934/mbe.2020081

Related Papers:

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.

References

[1]	P. A. Chirita, W. Nejdl and C. Zamfir, Preventing shilling attacks in online recommender systems, 2005 ACM International Workshop on Web Information and Data Management (WIDM), 2005. Available from: https://dlnext.acm.org/doi/abs/10.1145/1147376.1147387.
[2]	R. Burke, B. Mobasher, C. Williams and R. Bhaumik, Classification features for attack detection in collaborative recommender systems, 2006 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2006. Available from: https://dlnext.acm.org/doi/proceedings/10.1145/1150402.
[3]	C. A. Williams, B. Mobasher and R. Burke, Defending recommender systems: detection of profile injection attacks, SOCA, 1 (2007), 157-170.
[4]	B. Mehta, Unsupervised shilling detection for collaborative filtering, 2007 National Conference on Artificial Intelligence, 2007. Available from: https://www.aaai.org/Library/AAAI/2007/aaai07-222.php.
[5]	F. Peng, X. Zeng, H. Deng and L. Liu, Unsupervised detection of shilling attack for recommender system based on feature subset, Comput. Eng., 40 (2014), 109-114.
[6]	Z. Zhang and S. R. Kulkarni, Graph-based detection of shilling attacks in recommender systems, 2013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP), 2013. Available from: https://ieeexplore.ieee.org/xpl/conhome/6648476/proceeding.
[7]	Z. Wu, J. Cao, B. Mao and Y. Wang, Semi-SAD: Applying semi-supervised learning to shilling attack detection, 2011 ACM Conference on Recommender Systems (RecSys), 2011. Available from: https://dlnext.acm.org/doi/proceedings/10.1145/2043932.
[8]	C. Lv and W. Wang, Semi-supervised shilling attacks detection method based on SVM-KNN, Comput. Eng. Appl., 49 (2013), 7-10.
[9]	M. Gao, Q. Yuan, B. Ling and Q. Xiong, Detection of abnormal item based on time intervals for recommender systems, Sci. World J., 2014 (2014), 1-8.
[10]	Z. Yang and Z. Cai, Detecting abnormal profiles in collaborative filtering recommender systems, J. Intell. Inf. Syst., 48 (2017), 499-518.
[11]	W. Bhebe and O. P. Kogeda, Shilling attack detection in collaborative recommender systems using a meta learning strategy, 2015 International Conference on Emerging Trends in Networks and Computer Communications (ETNCC), 2015. Available from: https://ieeexplore.ieee.org/xpl/conhome/7164876/proceeding.
[12]	W. Zhou, J. Wen, Q. Xiong, M. Gao and J. Zeng, SVM-TIA a shilling attack detection method based on SVM and target item analysis in recommender systems, Neurocomputing, 210 (2016), 197-205.
[13]	M. O'Mahony, N. Hurley, N. Kushmerick and G. Silvestre, Collaborative recommendation: A robustness analysis, ACM Trans. Internet. Technol., 4 (2004), 344-377.
[14]	B. Mobasher, R. Burke, R. Bhaumik and C. Williams, Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness, ACM Trans. Internet. Technol., 7 (2007), 23-60.
[15]	R. T. Sikora and K. Chauhan, Estimating sequential bias in online reviews: A Kalman Filtering approach, Knowledge-Based Syst., 27(2012), 314-321.
[16]	K. Inuzuka, T. Hayashi and T. Takagi, Recommendation system based on prediction of user preference changes, 2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI), 2016. Available from: https://ieeexplore.ieee.org/xpl/conhome/7814734/proceeding.
[17]	C. Williams and B. Mobasher, Profile injection attack detection for securing collaborative recommender systems, DePaul Univ. CTI Technol. Repo., 2006 (2006), 1-47.
[18]	K. Fang and J. Wang, Shilling attacks detection algorithm based on nonnegative matrix factorization, Comput. Eng. Appl., 53 (2017), 150-154.

Reader Comments

Your name:*

Email:*
© 2020 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)