Title:Enhancing Recommendation Systems with Skew Deviation Bias for
Shilling Attack Detection
Volume: 18
Issue: 2
Author(s): Sarika Gambhir*, Sanjeev Dhawan and Kulvinder Singh
Affiliation:
- Department of Computer Engineering, University Institute of Engineering & Technology, Kurukshetra University, Kurukshetra, Haryana, India
Keywords:
Skewed deviation bias, support vector machine, shilling attack, collaborative filtering, recommender system.
Abstract:
Introduction: Recommender systems serve as a powerful tool to address the challenges
of information overload by delivering personalized recommendations. However, their susceptibility
to profile injection or shilling attacks poses a significant threat. Malicious entities can introduce
fabricated profiles into the database of users to manipulate the popularity of specific items,
subsequently influencing prediction outcomes
Method: Detecting and mitigating the impact of such attacks is critical for preserving recommendation
accuracy and user trust. The primary objective of this study is to develop an integrated
framework for robust shilling attack detection and data sparsity mitigation in recommendation
systems. This approach aims to make the system more resistant to manipulative attacks and improve
recommendation quality, especially when dealing with limited data. In this paper, Skew
Deviation Bias (SDB), is a novel metric that gauges the skewness within rating distributions, enabling
the identification of both fabricated shilling profiles and the anomalous rating behaviors exhibited
by attackers. Building upon this foundation, SDB is integrated with other statistical metrics
like Rating deviation from the mean agreement (RDMA), Weighted deviation from the mean
agreement (WDMA), Weighted degree of agreement (WDA), and length variance. This research
investigates the impact of incorporating SDB alongside existing attributes in countering various
attack scenarios, including random, average, and bandwagon attacks.
Result: Extensive experiments are conducted to compare the effectiveness of SDB when integrated
with existing attributes against scenarios employing only existing attributes. These experiments
cover a range of attack sizes while maintaining a fixed 50% filler size. The results of thorough
comparative analyses demonstrate the consistent superiority of the SDB-integrated approach,
resulting in higher accuracy across all attack types compared to scenarios using only existing attributes.
Notably, the random attack scenario shows the most significant accuracy improvement
among the evaluated scenarios.
Conclusion: The approach achieves a detection accuracy of 97.08% for random shilling attacks,
affirming its robustness. Furthermore, in the context of data sparsity, the approach notably enhances
recommendation quality.
