A shilling attack detector based on convolutional neural network for collaborative recommender system in social aware network
A shilling attack detector based on convolutional neural network for collaborative recommender system in social aware network
复制标题
基于卷积神经网络的社交感知网络协作推荐系统先令攻击检测器
DOI：
10.1093/comjnl/bxy008
复制
发表时间：
2018
期刊：
影响因子：
1.4
通讯作者：
Rodrigues Joel J P C
中科院分区：
文献类型：
--
作者：
Tong Chao;Yin Xiang;Li Jun;Zhu Tongyu;Lv Renli;Sun Liang;Rodrigues Joel J P C
在社会感知网络（SAN）范式中，最基本的任务之一是探索用户的属性和行为，这有助于设计更合适和有效的协议。特别是，在基于协同过滤的推荐系统等许多社会场景中，通过挖掘用户行为来检测先令攻击者是一个经常讨论的话题。由于协同过滤的性能完全基于用户提供的评分，因此很容易受到先令攻击，这种攻击会向评分数据库中注入有偏见的配置文件以改变系统。当前的先令攻击检测方法通过人为设计的特征来检测垃圾邮件用户，这些特征既不够健壮，也不够高效。本文提出了一种基于卷积神经网络的CNN-SAD方法，该方法利用变换后的网络结构从用户评分档案中挖掘深层特征。由于实现的深层特征比人为设计的特征更精确地详细说明用户评级，CNN-SAD可以更有效地检测先令攻击。实验结果表明，该方法能够准确检测绝大多数混淆攻击，并且优于其他最先进的算法，有助于SAN中的应用和安全性。
One of the most fundamental tasks in the socially aware network (SAN) paradigm is to explore the attributes and behavior of users, which helps to design more suitable and efficient protocols. Particularly, detection of shilling attackers by mining users’ behavior is a frequently discussed topic in many social scenes like recommender systems based on collaborative filtering. As the performances of collaborative filtering are entirely based on ratings provided by users, they are vulnerable to shilling attacks which perform injection of biased profiles into rating databases to alter the systems. Current shilling attack detection methods detect spam users through artificially designed features, which are neither robust nor efficient enough. This paper illustrates a novel convolutional neural network-based method named CNN-SAD, which applies transformed network structure to exploit deep-level features from users rating profiles. Since the achieved deep-level features elaborate users rating more precisely than artificially designed features, CNN-SAD can detect shilling attacks more efficiently. According to the experimental results, the proposed method is capable of detecting the vast majority of obfuscated attacks precisely and outperforms other state-of-the-art algorithms, which contributes to applications and security in SAN.