计算机科学 ›› 2016, Vol. 43 ›› Issue (10): 145-149.doi: 10.11896/j.issn.1002-137X.2016.10.027
董亚楠,刘学军,李斌
DONG Ya-nan, LIU Xue-jun and LI Bin
摘要： 在线广告是目前众多网络巨头收入的主要来源,在线广告也为网络的健康发展提供了强大的经济支撑。目前,利用用户行为属性特征来识别点击欺诈的方法中,含有较多的冗余特征,检测效率相对较低。针对这一问题,提出了一种属性特征选择与分类方法相结合的欺诈检测方法。通过训练数据集找到欺诈用户点击广告的属性特征集合,采用Fisher分方法得到了属性特征重要度排序,选取重要属性特征,并基于这些重要的特征使用支持向量机二分类方法分类。在真实数据集上的实验结果证明了该方法的可行性与有效性。
| [1] Chen Shi-guo,Zhang Dao-qiang.Experimental Comparisons of Semi-Supervised Dimensional Reduction Methods[J].Journal of Software,2011,2(1):28-43(in Chinese) 陈诗国,张道强.半监督降维方法的实验比较[J].软件学报,2011,22(1):28-43 [2] Haddadi H.Fighting online click-fraud using bluff ads[J].ACM SIGCOMM Computer Communication Review,2010,0(2):21-25 [3] Tuzhilin A.The Lane’s Gifts v.Google Report[EB/OL].2006[2013-03-01].http://googleblog.blogspot.com/pdf/Tuzh-ilin-Report.pdf [4] Qin Chao.Visitor action analyzing system for electronic business website[D].Shanghai:Shanghai Jiao Tong University,2006(in Chinese) 秦超.电子商务网站访客行为分析系统[D].上海:上海交通大学,2006 [5] Perera K S,Neupane B,Faisal M A,et al.A Novel Ensemble Learning-Based Approach for Click Fraud Detection in Mobile Advertising[M]∥Mining Intelligence and Knowledge Exploration.Springer International Publishing,2013:370-382 [6] Immorlica N,Jain K,Mahdian M,et al.Click Fraud ResistantMethods for Learning Click-Through Rates[C]∥Proceedings of the Workshop on Internet and Network Economics.Berlin Heidelberg:Springer,2005:34-45 [7] Oentaryo R,Lim E P,Finegold M,et al.Detecting Click Fraud in Online Advertising:A Data Mining Approach[J].Journal of Machine Learning Research,2014,14(1):99-140 [8] Hager M,Landergren T.Implementing best practices for frauddetection on an online advertising platform [D].Gothenburg:Chalmers University of Technology,2010 [9] Sergios T,Konstantinos K.Pattern recognition(2nd ed)[M].Salt Lake City:Elsevier Academic Press,1999 [10] Chang C C,Lin C J.LIBSVM:A library for support vector machines[C]∥ACM Transactions on Intelligent Systems and Technology.2011:389-396 [11] Ravisankar P,Ravi V,Raghava Rao G,et al.Detection of financial statement fraud and feature selection using data mining techniques[J].Decision Support Systems,2011,50(2):491-500 [12] Cortes C,Vapnik V.Support vector networks [J].MachineLearning,1995,20(3):273-297 [13] Zhang Yi-rong,Xian Ming,Xiao Shun-ping,et al.An Anomaly Intrusion Detection Technique of Support Vector Machine Based on Rough Set Attribute Reduction[J].Computer Science,2006,33(6):64-68(in Chinese) 张义荣,鲜明,肖顺平,等.一种基于粗糙集属性约简的支持向量异常入侵检测方法[J].计算机科学,2006,33(6):64-68 [14] Chang C C,Lin C J.LIBSVM:a library for support vector machines[J].ACM Transactions on Intelligent & Technology,2011,2(3):389-396 |
| No related articles found! |
|
||
首页