MDM-GANSA: A Multi-Distribution Generative Shilling Attack for Recommender Systems

Shilling attacks pose a significant threat to collaborative filtering recommender systems. However, fake user profiles generated by mainstream attack models often lack diversity and realism. Furthermore, the static noise strategies and statistical dependency modeling used in advanced frameworks like the Multi-Distribution Mixture Generative Adversarial Network (MDM-GAN) are ill-suited for high-dimensional, sparse attack scenarios. To address these challenges, we propose MDM-GANSA, a specialized attack model tailored for shilling attacks. First, it replaces the static mixture with a dynamic adaptive noise strategy by incorporating a weight predictor network. This network dynamically adjusts the weights of multiple noise sources based on the current training state, generating more diverse user latent representations. Second, it employs an autoencoder for data-driven dependency modeling, replacing the traditional statistical method. This allows the model to learn and generate profiles with inherent logical dependencies directly from genuine data. Consequently, it enhances the realism of the generated fake user profiles in terms of both statistical properties and internal logic. Additionally, the model utilizes an optimized two-stage generative architecture and fine-grained loss constraints to ensure training stability and high-quality outputs. Experimental results on two public datasets demonstrate that MDM-GANSA significantly outperforms various baseline models in both attack effectiveness and stealthiness. This study provides a concrete implementation for building a shilling-attack generation model targeting collaborative filtering recommender systems, and it also offers a feasible pathway for adapting general-purpose deep generative models to specialized security-oriented scenarios.