@inproceedings{0e5e7efea0104382a855f9a36cc4bc07,
title = "A Hybrid Abnormal Advertising Traffic Detection Method",
abstract = "Abnormal traffic is pervasive in the online advertising market. There are various cheating approaches while traditional anti-fraud methods are only effective for specific patterns. Combining the rule-based methods with supervised classification methods, we propose an abnormal traffic detection framework on both user layer and traffic layer. On the user layer, rule-based filters are designed to detect malicious users with duplicate clicks. We extract hybrid features under multi-granular time windows and train a user classifier to filter cheaters and complex spams indirectly. On traffic layer, we apply traffic filters to detect explicit fraudulent clicks and use a prediction model to detect malicious traffic with a higher precision. Extensive experiments on ground-truth data demonstrate the effectiveness of our detection method.",
keywords = "abnormal traffic, anti-fraud, hybrid features",
author = "Kun Wang and Guohai Xu and Chengyu Wang and Xiaofeng He",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 8th IEEE International Conference on Big Knowledge, ICBK 2017 ; Conference date: 09-08-2017 Through 10-08-2017",
year = "2017",
month = aug,
day = "30",
doi = "10.1109/ICBK.2017.50",
language = "英语",
series = "Proceedings - 2017 IEEE International Conference on Big Knowledge, ICBK 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "236--241",
editor = "Xindong Wu and Xindong Wu and Tamer Ozsu and Jim Hendler and Ruqian Lu",
booktitle = "Proceedings - 2017 IEEE International Conference on Big Knowledge, ICBK 2017",
address = "美国",
}