Robust and Adaptive Dual-Defense Framework Against Data Poisoning Attacks in Recommendation Systems

Deep learning-based recommendation systems are highly vulnerable to data poisoning attacks, where adversaries manipulate user interactions to degrade model integrity. We hypothesize that combining an active robust loss with a passive GAN-based detection will significantly reduce poisoning impact in recommendation systems without sacrificing utility. We propose a robust and adaptive dual-defense framework: the active defense integrates a crafted loss function to mitigate poisoning effects while maintaining model performance. The passive defense employs a Generative Adversarial Network (GAN)-based detection model to identify and filter poisoned data, enhancing detection accuracy and system security. The framework supports classical matrix factorization (MF) model and large language model (LLM)-based pipelines and scales to large datasets. Extensive experiments across multiple real-world datasets at varying poison rates show that our method outperforms representative defenses, consistently reducing attack success without sacrificing recommendation quality. The framework also admits a federated instantiation, where robust training and GAN-based detection run on clients and only privacy-preserving summaries are aggregated. The proposed method significantly improves the robustness and adaptability of recommendation systems under data poisoning attacks.