Re-scale AdaBoost for Attack Detection in Collaborative Filtering Recommender Systems

TL;DR

This paper introduces RAdaBoost, a re-scaled AdaBoost variant, combined with well-designed features, to improve attack detection in collaborative filtering recommender systems, especially under imbalanced attack scenarios.

Contribution

The paper proposes a novel RAdaBoost method with feature extraction tailored for attack detection in CFRSs, enhancing performance over classical techniques.

Findings

RAdaBoost outperforms SVM, kNN, and AdaBoost in experiments.

Feature extraction based on attack models improves classification accuracy.

Method effectively handles imbalanced attack detection scenarios.

Abstract

Collaborative filtering recommender systems (CFRSs) are the key components of successful e-commerce systems. Actually, CFRSs are highly vulnerable to attacks since its openness. However, since attack size is far smaller than that of genuine users, conventional supervised learning based detection methods could be too "dull" to handle such imbalanced classification. In this paper, we improve detection performance from following two aspects. First, we extract well-designed features from user profiles based on the statistical properties of the diverse attack models, making hard classification task becomes easier to perform. Then, refer to the general idea of re-scale Boosting (RBoosting) and AdaBoost, we apply a variant of AdaBoost, called the re-scale AdaBoost (RAdaBoost) as our detection method based on extracted features. RAdaBoost is comparable to the optimal Boosting-type algorithm and can effectively improve the performance in some hard scenarios. Finally, a series of experiments on the MovieLens-100K data set are conducted to demonstrate the outperformance of RAdaBoost comparing with some classical techniques such as SVM, kNN and AdaBoost.