Deep Forest-Based E-Commerce Recommendation Attack Detection Model

Abstract

With the advancement of Internet, recommendation systems have become an indispensable component for every e-commerce platform, playing an increasingly pivotal role in product recommendations. However, due to the recommendation mechanisms of these systems, numerous new attack patterns have emerged. A novel attack pattern targeting e-commerce recommendation systems, termed the “Ride Item’s Coattails” attack, fabricates false click information to deceitfully establish associations between popular products and low-quality products, intending to mislead the e-commerce platform’s recommendation system and promote the sales of substandard products. This article presents a recommendation system attack detection method based on the Deep Forest algorithm to address the challenges of these novel recommendation system attacks. Random forests are used for feature selection, aiming to filter crucial features and reduce feature redundancy. To tackle the issue of extreme class imbalance, a symmetric sampling technique based on k-means centroids is introduced. This approach addresses the incomplete noise filtering and sampling data comprehensiveness issues commonly found in undersampling algorithms. Considering the potential for even more imbalanced data in real-world scenarios, a combined strategy of undersampling and SMOTE resampling is used to handle imbalanced data. The proposed algorithm was trained on e-commerce “Ride Item’s Coattails” attack identification data from Alibaba Cloud’s Tianchi, which originates from genuine recommendation system attack data. The proposed method was compared with various deep learning and machine learning algorithms, such as DLMP and deep neural networks (DNN), for extensive validation. Experiments demonstrate that the proposed technique effectively meets the demands for attack detection.