Making large-scale support vector machine learning practical
Advances in kernel methods
Network traffic anomaly detection based on packet bytes
Proceedings of the 2003 ACM symposium on Applied computing
Characterization of network-wide anomalies in traffic flows
Proceedings of the 4th ACM SIGCOMM conference on Internet measurement
ACAS: automated construction of application signatures
Proceedings of the 2005 ACM SIGCOMM workshop on Mining network data
Data streaming algorithms for estimating entropy of network traffic
SIGMETRICS '06/Performance '06 Proceedings of the joint international conference on Measurement and modeling of computer systems
Combining filtering and statistical methods for anomaly detection
IMC '05 Proceedings of the 5th ACM SIGCOMM conference on Internet Measurement
Detecting anomalies in network traffic using maximum entropy estimation
IMC '05 Proceedings of the 5th ACM SIGCOMM conference on Internet Measurement
ACM Computing Surveys (CSUR)
Outside the Closed World: On Using Machine Learning for Network Intrusion Detection
SP '10 Proceedings of the 2010 IEEE Symposium on Security and Privacy
Aiding the detection of fake accounts in large scale social online services
NSDI'12 Proceedings of the 9th USENIX conference on Networked Systems Design and Implementation
Covariance-Matrix Modeling and Detecting Various Flooding Attacks
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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Online video-on-demand (VoD) services invariably maintain a view count for each video they serve, and it has become an important currency for various stakeholders, from viewers, to content owners, advertizers, and the online service providers themselves. There is often significant financial incentive to use a robot (or a botnet) to artificially create fake views. How can we detect the fake views? Can we detect them (and stop them) efficiently? What is the extent of fake views with current VoD service providers? These are the questions we study in this paper. We develop some algorithms and show their effectiveness for this problem.