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Detecting coalition attacks in online advertising: A hybrid data mining approach

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  • Coalition attack is nowadays one of the most common type of attacks in the industry of online advertising. In this paper, we attempt to mitigate the problem of frauds by proposing a hybrid framework that detects the coalition attacks based on multiple metrics. We also articulate the theoretical basis for these metrics to be integrated into the hybrid framework. Furthermore, we instance the framework with two metrics and develop a detection system that identifies the coalition attacks from two distinguish perspectives.
    Mathematics Subject Classification: Primary: 93E10, 93E35; Secondary: 62M20, 62H30.


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  • [1]

    L. Adamic and E. Adar, Friends and neighbors on the web, Social Networks, 25 (2003), 211-230.doi: 10.1016/S0378-8733(03)00009-1.


    V. Anupam, A. Mayer, K. Nissim and B. Pinkas, On the security of pay-per-click and other web advertising schemes, in The 8th International Conference on World Wide Web, 31 (1999), 1091-1100.doi: 10.1016/S1389-1286(99)00023-7.


    M. S. Charikar, Similarity estimation techniques from rounding algorithms, in Proceedings of the Thiry-fourth Annual ACM Symposium on Theory of Computing, (2002), 380-388.doi: 10.1145/509907.509965.


    N. Daswani, C. Mysen, V. Rao, S. Weis, K. Gharachorloo and S. Ghosemajumde, Online advertising fraud, in Crimeware: Understanding New Attacks and Defenses, Addison-Wesley Professional, Reading, 2008.


    M. A. Hasan, A survey of link prediction in social networks, Social Network Data Analytics, (2011), 243-275.doi: 10.1007/978-1-4419-8462-3_9.


    B. Kitts, J. Y. Zhang, A. Roux and R. Mills, Click fraud detection with bot signatures, in Proceedings of ISI 2013, Seattle, Washington, USA, (2013), 146-150.doi: 10.1109/ISI.2013.6578805.


    C. Kim, H. Miao and K. Shim, CATCH: A detecting algorithm for coalition attacks of hit inflation in internet advertising, Information Systems, 36 (2011), 1105-1123.doi: 10.1016/j.is.2011.04.001.


    C. K. S. Leung, Anti-monotone constraints, in Encyclopedia of Database Systems (eds. Ling Liu and M. Tamer Özsu), Springer, (2009), 98-98.


    K. Lee, H. Choi and B. Moon, Parallel data processing with MapReduce: a survey, in The ACM Special Interest Group on Management of Data Record, 40 (2011), 11-20.doi: 10.1145/2094114.2094118.


    A. Metwally, D. Agrawal and A. EI Abbadi, Duplicate detection in click streams, in International World Wide Web Conference, (2005), 12-21.doi: 10.1145/1060745.1060753.


    A. Metwally, D. Agrawal and A. EI Abbadi, Using association rules for fraud detection in web advertising networks, in Proceedings of the 31st international conference on very large data bases, (2005), 169-180.


    A. Metwally, D. Agrawal and A. EI Abbadi, Detectives: detecting coalition hit inflation attacks in advertising networks streams, in Proceedings of the 16th International Conference on World Wide Web, (2007), 241-250.doi: 10.1145/1242572.1242606.


    A. Metwally, D. Agrawal, A. EI Abbadi and Q. Zheng, On hit inflation techniques and detection in streams of web advertising networks, in Proceedings of the 27th International Conference on Distributed Computing Systems, (2007), 52-52.doi: 10.1109/ICDCS.2007.124.


    C. Phua, E. Y. Cheu, G. E. Yap, K. Sim and M. N. Nguyen, Feature engineering for click fraud detection, in ACML Workshop on Fraud Detection in Mobile Advertising, 2012.


    Y. Peng, L. Zhang, J. M. Chang and Y. Guan, An effective method for combating malicious scripts clickbots, in Computer Security, the Series Lecture Notes in Computer Science, 5789 (2009), 523-538.doi: 10.1007/978-3-642-04444-1_32.


    B. K. Perera, A Class Imbalance Learning Approach to Fraud Detection in Online Advertising, M.Sc. thesis, Masdar Institute of Science and Technology, 2013.


    K. Springborn and P. Barford, Impression fraud in online advertising via pay-per-view networks, in Proceedings of the 22nd USENIX Security Symposium, (2013), 211-226.


    F. Soldo and A. Metwally, Traffic anomaly detection based on the IP size distribution, in Proceedings of the INFOCOM International Conference on Computer Communications, Joint Conference of the IEEE Computer and Communications Societies, (2012), 2005-2013.doi: 10.1109/INFCOM.2012.6195581.


    C. Walgampaya and M. Kantardzic, Cracking the smart clickbot, in Proceedings of the 13th IEEE International Symposium on Web Systems Evolution, (2011), 125-134.doi: 10.1109/WSE.2011.6081830.


    C. Walgampaya and M. Kantardzic and R. Yampolskiy, Evidence fusion for real time click fraud detection and prevention, in Intelligent Automation and Systems Engineering, Lecture Notes in Electrical Engineering, Springer Science+Business Media, 103 (2011), 1-14.doi: 10.1007/978-1-4614-0373-9_1.


    Q. Zhang and W. Feng, Detecting coalition frauds in online-advertising, in Mathematical and Computational Approaches in Advancing Modern Science and Engineering, (eds. Jacques Bélair, Ian A. Frigaard, Herb Kunze, Roman Makarov, Roderick Melnik and Raymond J. Spiteri) Springer, (2016), 595-605.doi: 10.1007/978-3-319-30379-6_54.


    L. Zhang and Y. Guan, Detecting click fraud in pay-per-click streams of online advertising networks, in The 28th International Conference on Distributed Computing Systems, (2008), 77-84.doi: 10.1109/ICDCS.2008.98.

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