Abstract
Due to the rapid technological changes, these days collaborative filtering-based recommender systems are being widely used worldwide. Collaborative filtering approach is more vulnerable from being attacked because of its open nature. The attackers may rate the fake ratings to disturb the systems. In this paper, unsupervised Kohonen Self-Organizing Map (KSOM) clustering technique is used to make a better detection between genuine and fake profiles to reduce profile injection attacks and compared with existing techniques Enhanced Clustering Large Applications Based on Randomized Search (ECLARANS) and Partition Around Medoids (PAM) with variants of attack size. It has been noticed that KSOM outperforms over ECLARANS and PAM techniques with good outlier accuracy.
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Notes
- 1.
“MovieLens dataset” available at https://movielens.org/ accessed on April 12, 2018.
In ECLARANS algorithm, the following size of clusters has been generated which is shown in Fig. 2. Here, we see that cluster A and cluster B are small-size clusters in Table 2. This can be seen that user is included in clusters A and B considering the attack profiles.
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Acknowledgements
I give my warm thanks to Dr. Veer Sain Dixit as research supervisor for his idea about SOM and encouragement of writing this paper. Without his valuable support, this work is not possible toward enhancement in the knowledge about profile injection attack detection.
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Verma, A.K., Dixit, V.S. (2020). An Outlier Accuracy Improvement in Shilling Attacks Using KSOM. In: Choudhury, S., Mishra, R., Mishra, R., Kumar, A. (eds) Intelligent Communication, Control and Devices. Advances in Intelligent Systems and Computing, vol 989. Springer, Singapore. https://doi.org/10.1007/978-981-13-8618-3_38
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