Abstract: Recommendation systems have been widely used in most Internet platforms,such as e-commerce,social media,and information sharing,which effectively solve the problem of information overload.However,these platforms are open to all Internet users,leading to illegal manipulation of rating data through malicious interference and deliberate attacks by unscrupulous users using system design flaws,affecting the recommendation results and seriously jeopardizing the security of recommendation ser-vices.Most existing detection methods are based on manually constructed features extracted from rating data for shilling attack detection,which is challenging to adapt to more complex co-visitation injection attacks,and manually constructed features are time-consuming and need more differentiation capability.In contrast,the scale of attack behavior is much smaller than normal behavior,bringing imbalanced data problems to traditional detection methods.Therefore,the paper proposes stacked multilayer graph convolutional neural networks end-to-end to learn multi-order interaction behavior information between users and items to obtain user embeddings and item embeddings,which are used as attack detection features,and convolutional neural networks are used as base classifiers to achieve deep behavior feature extraction,combined with ensemble methods to detect attacks.Experimental results on real datasets show that the method better detects co-visitation injection attacks and overcomes the imbalanced data problem to a certain extent compared with popular malicious attack detection methods for recommendation systems.

Key words: Attack detection, Co-visitation injection attack, Recommendation systems, Graph convolutional neural networks, Convolutional neural networks, Ensemble methods