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

Authors: M. Sunitha; Naramula Venkatesh

Addresses: School of Computer Science and Artificial Intelligence, Department of CSE, SR University, Warangal-506371, Telangana, India ' School of Computer Science and Artificial Intelligence, Department of CSE, SR University, Warangal-506371, Telangana, India

Abstract: 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.

Keywords: shilling attacks; DuoNet; T-Bi-LSTM; OCNN; improved seagull optimisation algorithm; ISOA; temporal features and spatial features.

DOI: 10.1504/IJICS.2026.153781

International Journal of Information and Computer Security, 2026 Vol.30 No.1, pp.85 - 113

Received: 26 Jun 2025
Accepted: 15 Nov 2025
Published online: 26 May 2026 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article