July  2016, 1(2&3): 227-245. doi: 10.3934/bdia.2016006

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

1. 

Department of Computing & Information Systems, Department of Mathematics, Trent University, Peterborough, Ontario K9J 0G2, Canada, Canada

Received  February 2016 Revised  September 2016 Published  September 2016

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.
Citation: Qinglei Zhang, Wenying Feng. Detecting coalition attacks in online advertising: A hybrid data mining approach. Big Data & Information Analytics, 2016, 1 (2&3) : 227-245. doi: 10.3934/bdia.2016006
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N. Daswani, C. Mysen, V. Rao, S. Weis, K. Gharachorloo and S. Ghosemajumde, Online advertising fraud,, in Crimeware: Understanding New Attacks and Defenses, (2008). Google Scholar

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

[12]

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. doi: 10.1016/j.is.2011.04.001. Google Scholar

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

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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. doi: 10.1145/2094114.2094118. Google Scholar

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

[16]

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. Google Scholar

[17]

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. doi: 10.1145/1242572.1242606. Google Scholar

[18]

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. doi: 10.1109/ICDCS.2007.124. Google Scholar

[19]

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). Google Scholar

[20]

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

[21]

B. K. Perera, A Class Imbalance Learning Approach to Fraud Detection in Online Advertising,, M.Sc. thesis, (2013). Google Scholar

[22]

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. Google Scholar

[23]

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

[24]

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

[25]

C. Walgampaya and M. Kantardzic and R. Yampolskiy, Evidence fusion for real time click fraud detection and prevention,, in Intelligent Automation and Systems Engineering, 103 (2011), 1. doi: 10.1007/978-1-4614-0373-9_1. Google Scholar

[26]

Q. Zhang and W. Feng, Detecting coalition frauds in online-advertising,, in Mathematical and Computational Approaches in Advancing Modern Science and Engineering, (2016), 595. doi: 10.1007/978-3-319-30379-6_54. Google Scholar

[27]

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. doi: 10.1109/ICDCS.2008.98. Google Scholar

show all references

References:
[1]

, Zenithoptimedia,, Available from , (). Google Scholar

[2]

, Doubleclick by Google,, Available from: , (). Google Scholar

[3]

, Rightmedia from Yahoo!,, Available from: , (). Google Scholar

[4]

, Disco MapReduce,, Available from , (). Google Scholar

[5]

, Hadoop MapReduce,, Available from: , (). Google Scholar

[6]

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

[7]

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. doi: 10.1016/S1389-1286(99)00023-7. Google Scholar

[8]

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

[9]

N. Daswani, C. Mysen, V. Rao, S. Weis, K. Gharachorloo and S. Ghosemajumde, Online advertising fraud,, in Crimeware: Understanding New Attacks and Defenses, (2008). Google Scholar

[10]

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

[11]

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

[12]

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. doi: 10.1016/j.is.2011.04.001. Google Scholar

[13]

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

[14]

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. doi: 10.1145/2094114.2094118. Google Scholar

[15]

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

[16]

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. Google Scholar

[17]

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. doi: 10.1145/1242572.1242606. Google Scholar

[18]

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. doi: 10.1109/ICDCS.2007.124. Google Scholar

[19]

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). Google Scholar

[20]

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

[21]

B. K. Perera, A Class Imbalance Learning Approach to Fraud Detection in Online Advertising,, M.Sc. thesis, (2013). Google Scholar

[22]

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. Google Scholar

[23]

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

[24]

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

[25]

C. Walgampaya and M. Kantardzic and R. Yampolskiy, Evidence fusion for real time click fraud detection and prevention,, in Intelligent Automation and Systems Engineering, 103 (2011), 1. doi: 10.1007/978-1-4614-0373-9_1. Google Scholar

[26]

Q. Zhang and W. Feng, Detecting coalition frauds in online-advertising,, in Mathematical and Computational Approaches in Advancing Modern Science and Engineering, (2016), 595. doi: 10.1007/978-3-319-30379-6_54. Google Scholar

[27]

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. doi: 10.1109/ICDCS.2008.98. Google Scholar

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