Shilling Attacks and Fake Reviews Injection: Principles, Models, and Datasets

Abstract

Recommendation systems have proved to be a compelling performance in overcoming the data overload problem in many domains, such as e-commerce, e-health, and transportation. Recommender systems guide users/clients to personalized recommendations based on their preferences. However, some recommendation systems are vulnerable to shilling attacks, which create rating biases or fake reviews that will eventually affect the authenticity and integrity of the generated recommendations. This survey comprehensively covers various shilling attack methods, including high-knowledge, low-knowledge attacks, and obfuscated attacks. It explores malicious review generators that generate fake text. In addition to that, this survey covers shilling attack detection methods such as supervised, unsupervised, semisupervised, and hybrid techniques. Natural Language Processing techniques are also thoroughly explored for fake text review detection using large language models (LLMs). A wide range of detection mechanisms incorporated in the literature is examined, such as convolutional neural network (CNN), long short term memory (LSTM)-based detectors for rating-based shilling attacks, and bidirectional encoder representation (BERT) and RoBERTa-based detectors for fake reviews that are accompanied by shilling attacks, aiming to offer insights into the evolving methods of shilling attack strategies and the corresponding advancements in the detection methods.