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首页> 外文期刊>Journal of ambient intelligence and humanized computing >Hybrid convolutional neural network (CNN) and long-short term memory (LSTM) based deep learning model for detecting shilling attack in the social-aware network
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Hybrid convolutional neural network (CNN) and long-short term memory (LSTM) based deep learning model for detecting shilling attack in the social-aware network

机译：混合卷积神经网络（CNN）和基于长短短期存储器（LSTM）的深度学习模型，用于检测社交意识网络的先令攻击

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摘要

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.
机译：在社交意识网络（SAN）范式中，基本活动集中在探索用户的行为和属性。这次对高效和合适的协议设计的用户特性辅助。特别是，先令攻击将高度脆弱性引入推荐系统。先令攻击者使用评论，用户评级和伪造用户生成的内容数据，以计算推荐排名。在推荐系统中检测先令攻击被认为是维持其公平性和可靠性的必要条件。具体而言，通过有效的用户行为挖掘来检测先令攻击者的协同过滤策略被认为是文献中的主要方法。在本文中，提出了一种混合卷积神经网络（CNN）和长期存储器（LSTM）的深度学习模型（CNN-LSTM），用于检测推荐系统中的先令攻击。这种深度学习模型利用转换的网络架构来利用从用户额定的配置文件派生的深层属性。它克服了现有的先令攻击检测方法的局限性，它主要专注于通过人工设计特征来识别垃圾邮件用户，以提高其效率和鲁棒性。在阐明深层次的功能中，它也是有效的，以通过准确地阐述用户评级来有效地检测先令攻击。实验结果证实了所提出的CNN-LSTM方法通过准确地检测到与用于调查的最新算法相比的大部分混淆攻击。

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