Hybrid convolutional neural network (CNN) and long-short term memory (LSTM) based deep learning model for detecting shilling attack in the social-aware network

Abstract

In social aware network (SAN) paradigm, the fundamental activities concentrate on exploring the behavior and attributes of the users. This investigation of user characteristic aids in the design of highly efficient and suitable protocols. In particular, the shilling attack introduces a high degree of vulnerability into the recommender systems. The shilling attackers use the reviews, user ratings and forged user generated content data for the computation of recommendation rankings. The detection of shilling attack in recommender systems is considered to be essential for sustaining their fairness and reliability. In specific, the collaborative filtering strategies utilized for detecting shilling attackers through efficient user behavior mining are considered as the predominant methodologies in the literature. In this paper, a hybrid convolutional neural network (CNN) and long-short term memory (LSTM)-based deep learning model (CNN–LSTM) is proposed for detecting shilling attack in recommender systems. This deep learning model utilizes the transformed network architecture for exploiting the deep-level attributes derived from user rated profiles. It overcomes the limitations of the existing shilling attack detection methods which mostly focuses on identifying spam users by designing features artificially in order to enhance their efficiency and robustness. It is also potent in elucidating deep-level features for efficiently detecting shilling attacks by accurately elaborating the user ratings. The experimental results confirmed the significance of the proposed CNN–LSTM approach by accurately detecting most of the obfuscated attacks compared to the state-of-art algorithms used for investigation.