基于TextCNN的先令攻击模型

Dongfang Hu, Bin Xu, Jun Wang, Linfeng Han, Jiayi Liu
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引用次数: 1

摘要

随着互联网的发展,互联网上的信息量迅速增加,这使得人们很难选择自己真正想要的信息。推荐系统是解决这一问题的有效途径。假用户可以被不法分子注入攻击推荐系统;因此,准确识别虚假用户是推荐系统的必要特征。现有的假用户检测算法侧重于针对不同类型的攻击设计识别方法,对未知攻击或混合攻击的检测能力有限。使用深度学习模型可以自动提取虚假用户评分特征,但神经网络模型不适用于离散用户评分数据。本文采用随机行走的方法,将原本离散的用户评分数据重新排列成具有空间连续性的评分特征矩阵。评级数据与文本数据在分布模式上有一定的相似性。通过有效的类比,可以将原本用于NLP领域的TextCNN模型进行改进,并应用于评价特征矩阵的分类任务。结合随机行走和词向量处理的思想,提出了一种针对用户评分数据的TextCNN检测模型。为了验证该模型的有效性,在MoiveLens数据集上针对7种不同的攻击检测算法对该模型进行了测试,与4种攻击检测算法相比,该模型表现出更好的性能。特别是对于Aop攻击,该模型以F1值为评价指标,具有接近100%的检测性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Shilling Attack Model Based on TextCNN
With the development of the Internet, the amount of information on the Internet is increasing rapidly, which makes it difficult for people to select the information they really want. A recommendation system is an effective way to solve this problem. Fake users can be injected by criminals to attack the recommendation system; therefore, accurate identification of fake users is a necessary feature of the recommendation system. Existing fake user detection algorithms focus on designing recognition methods for different types of attacks and have limited detection capabilities against unknown or hybrid attacks. The use of deep learning models can automate the extraction of false user scoring features, but neural network models are not applicable to discrete user scoring data. In this paper, random walking is used to rearrange the otherwise discrete user rating data into a rating feature matrix with spatial continuity. The rating data and the text data have some similarity in the distribution mode. By effective analogy, the TextCNN model originally used in NLP domain can be improved and applied to the classification task of rating feature matrix. Combining the ideas of random walking and word vector processing, this paper proposes a TextCNN detection model for user rating data. To verify the validity of the proposed model, the model is tested on MoiveLens dataset against 7 different attack detection algorithms, and exhibits better performance when compared with 4 attack detection algorithms. Especially for the Aop attack, the proposed model has nearly 100% detection performance with F1 – value as the evaluation index.
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