DuoNet: a hybrid deep learning model for shilling attack detection in recommendation systems

The study proposes a hybrid deep learning model, DuoNet, designed to detect and mitigate shilling attacks effectively. Data is collected from social media networks and e-commerce platforms, capturing user-item rating interactions. The pre-processing stage involves removing duplicate entries, imputing missing values using mean imputation and scaling the data with the min-max normalisation technique to ensure consistency. DuoNet integrates two advanced methodologies: T-Bi-LSTM for extracting temporal features and OCNN for capturing spatial features. The improved seagull optimisation algorithm (ISOA) optimises the CNN's hyperparameters, enhancing the model's overall performance. The classification layer in the CNN combines temporal and spatial features to predict whether a user profile is genuine or represents a shilling attack. Experimental evaluations conducted on datasets from Amazon and Netflix demonstrate that DuoNet outperforms existing models, achieving higher accuracy, precision, F1-score, recall, and specificity.