Visible to the public Detection Of Shilling Attack In Collaborative Filtering Recommender System By Pca And Data Complexity

TitleDetection Of Shilling Attack In Collaborative Filtering Recommender System By Pca And Data Complexity
Publication TypeConference Paper
Year of Publication2018
AuthorsZhang, F., Deng, Z., He, Z., Lin, X., Sun, L.
Conference Name2018 International Conference on Machine Learning and Cybernetics (ICMLC)
ISBN Number978-1-5386-5214-5
Keywordsauthentic profiles, CF recommender system, Collaboration, collaborative filtering, collaborative filtering recommender system, Complexity theory, Correlation, data complexity, feature extraction, gaussian distribution, human factors, PCA, personalized recommendation, principal component analysis, pubcrawl, recommender system, recommender systems, resilience, Resiliency, Scalability, security of data, Shilling attack detection, shilling attacks detection, suspected attack profiles, unsupervised detection method, unsupervised PCA

Collaborative filtering (CF) recommender system has been widely used for its well performing in personalized recommendation, but CF recommender system is vulnerable to shilling attacks in which shilling attack profiles are injected into the system by attackers to affect recommendations. Design robust recommender system and propose attack detection methods are the main research direction to handle shilling attacks, among which unsupervised PCA is particularly effective in experiment, but if we have no information about the number of shilling attack profiles, the unsupervised PCA will be suffered. In this paper, a new unsupervised detection method which combine PCA and data complexity has been proposed to detect shilling attacks. In the proposed method, PCA is used to select suspected attack profiles, and data complexity is used to pick out the authentic profiles from suspected attack profiles. Compared with the traditional PCA, the proposed method could perform well and there is no need to determine the number of shilling attack profiles in advance.

Citation Keyzhang_detection_2018