基于项目流行度和新颖度分类特征的托攻击检测算法

针对有监督检测方法在检测托攻击时准确率不高的问题，提出一种基于项目流行度和新颖度分类特征的托攻击检测算法。首先，根据真实概貌和攻击概貌在选择评分项目方式上不同，从流行度和新颖度角度，提出有效区分正常用户和攻击用户的特征；然后，基于这些特征提出一种集成检测框架，通过Boosting提升技术产生多个差异较大的基分类器，并且通过融合带有权重的基分类器的预测值得到最终的检测结果。实验结果表明，基于项目流行度和新颖度分类特征的托攻击检测算法能够提高攻击检测的准确率和召回率。

An Shilling Attack Detection Algorithm Based on Item Popularity and Novelty Degree Features

The existing supervised detection algorithms have low precision when they detect shilling attacks.To address this problem,a shilling attack detection algorithm based on the features of popularity and novelty degree was proposed.Firstly,according to the difference between genuine and attack profiles in choosing items to rate,several features were extracted,which can effectively distinguish normal and attack users in perspective of popularity and novelty.Secondly,an ensemble detection framework based on these features was proposed.The Boosting technology was used to generate different base classifiers and the detection results were obtained by combining the predicted results of the base classifiers with weight.The experimental results showed that the shilling attack detection algorithm based on the features of popularity and novelty degree can improve the precision and recall of attack detection.