Network for Shilling attack Detection

User similarity-based graph convolutional neural network for shilling attack detection

Collaborative recommendation systems have been widely used in various fields, such as movies, music and e-commerce. However, due to the natural openness of its ratings, it is vulnerable to shilling attacks. Shilling attacks greatly affect the accuracy and trustworthiness of recommendation systems, so we urgently need effective methods to counter shilling attacks. Some detection methods have been proposed previously.

However, they mostly use manual feature extraction-based methods. These methods require specialized statistical knowledge to summarize user-specific rating patterns in user rating databases, which is very difficult. Thus, we propose a method called User Similarity-based Graph convolutional neural network for Shilling Attack Detection (USGSAD). This method achieves the purpose of detecting shilling attacks without using manual features.

First, our method calculates user similarity by jointing both correlation and deviation of user rating behaviors. Second, we build a user relationship graph based on user similarity matrix and use graph embedding method to obtain user low-dimensional embedding vectors. Finally, we design a User Similarity Graph Convolutional Network (USGCN) to assign weights to aggregate user embeddings and predict the attackers in the recommender system. Adequate experiments on Amazon and MovieLens datasets show that our proposed method outperforms the baseline methods in detection performance.

backpropagation, activation function, learning rate, epoch, weight initialization, loss function, overfitting, underfitting, regularization, dropout, feedforward network, convolutional neural network, recurrent neural network, long short-term memory, gradient descent, batch normalization, transfer learning, model optimization, data augmentation, and hyperparameter tuning

#NeuralNetworks, #DeepLearning, #AI, #MachineLearning, #Backpropagation, #CNN, #RNN, #LSTM, #GradientDescent, #ActivationFunction, #LossFunction, #Epochs, #Overfitting, #Regularization, #ModelTraining, #TransferLearning, #DataAugmentation, #BatchNormalization, #HyperparameterTuning, #AIResearch

International Conference on Network Science and Graph Analytics

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