基于评分离散度的托攻击检测算法

检测托攻击的本质是对真实用户和虚假用户进行分类,现有的检测算法对于具有选择项的流行攻击、段攻击等攻击方式的检测鲁棒性较差.针对这一问题,通过分析真实用户和虚假用户的评分分布情况,结合ID3决策树提出基于用户评分离散度的托攻击检测Dispersion-C算法.算法通过用户评分极端评分比、去极端评分方差和用户评分标准差3个...

A shilling attack detection algorithm based on score dispersion

The essence of detecting shilling attacks is to classify real users and fake users. The existing detection algorithms have poor detection robustness against bandwagon attacks and segment attacks with options. To solve this problem, by analyzing the different distributions of ratings of real users and fake users, combined with ID3 decision tree, a shilling attack detection algorithm based on user score dispersion is proposed. The algorithm measures the dispersion of user scores by using three features: extreme score ratio, de-extreme score variance, and user score standard deviation, and uses it as the classification feature of the ID3 decision tree algorithm. According to the information gain of different features, the feature is selected as the classification attribute, and the classifier is trained. Experiments show that the algorithm has a good detection effect on all kinds of shilling attacks, and has good robustness.