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Improving the Shilling Attack Detection in Recommender Systems Using an SVM Gaussian Mixture Model

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  • Jasem M. Alostad

    (College of Basic Education, The Public Authority of Applied Education and Training, Safat 13092, Kuwait)

Abstract

With recent advances in e-commerce platforms, the information overload has grown due to increasing number of users, rapid generation of data and items in the recommender system. This tends to create serious problems in such recommender systems. The increasing features in recommender systems pose some new challenges due to poor resilience to mitigate against vulnerable attacks. In particular, the recommender systems are more prone to be attacked by shilling attacks, which creates more vulnerability. A recommender system with poor detection of attacks leads to a reduced detection rate. The performance of the recommender system is thus affected with poor detection ability. Hence, in this paper, we improve the resilience against shilling attacks using a modified Support Vector Machine (SVM) and a machine learning algorithm. The Gaussian Mixture Model is used as a machine learning algorithm to increase the detection rate and it further reduces the dimensionality of data in recommender systems. The proposed method is evaluated against several result metrics, such as the recall rate, precision rate and false positive rate between different attacks. The results of the proposed system are evaluated against probabilistic recommender approaches to demonstrate the efficacy of machine learning language in recommender systems.

Suggested Citation

  • Jasem M. Alostad, 2019. "Improving the Shilling Attack Detection in Recommender Systems Using an SVM Gaussian Mixture Model," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 18(01), pages 1-18, March.
  • Handle: RePEc:wsi:jikmxx:v:18:y:2019:i:01:n:s0219649219500114
    DOI: 10.1142/S0219649219500114
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    References listed on IDEAS

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    1. Chong Ju Choi & Carla C. J. M. Millar & Caroline Y. L. Wong, 2005. "Knowledge and the State," Palgrave Macmillan Books, in: Knowledge Entanglements, chapter 0, pages 19-38, Palgrave Macmillan.
    2. Yu, Junliang & Gao, Min & Rong, Wenge & Li, Wentao & Xiong, Qingyu & Wen, Junhao, 2017. "Hybrid attacks on model-based social recommender systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 483(C), pages 171-181.
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