June 25, 2007 to June 27, 2007
Minos Garofalakis , Yahoo! Research
Ling Huang , UC Berkeley
Nina Taft , Intel Research Berkeley
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDCS.2007.93
In recent work, we proposed D-Trigger, a framework for tracking a global condition over a large network that allows us to detect anomalies while only collecting a very limited amount of data from distributed monitors. In this paper, we expand our previous work by designing a new class of queries (conditions) that can be tracked for anomaly violations. We show how security violations can be detected over a time window of any size. This is important because security operators do not know in advance the window of time in which measurements should be made to detect anomalies. We also present an algorithm that determines how each machine should filter its time series measurements before back-hauling them to a central operations center. Our filters are computed analytically such that upper bounds on false positive and missed detection rates are guaranteed. In our evaluation, we show that botnet detection can be carried out successfully over a distributed set of machines, while simultaneously filtering out 80 to 90% of the measurement data.
Distributed Triggering, Network Monitoring, Anomaly Detection, Data Aggregation, Queueing Theory.
Minos Garofalakis, Ling Huang, Nina Taft, "Communication-Efficient Tracking of Distributed Cumulative Triggers", ICDCS, 2007, 2013 IEEE 33rd International Conference on Distributed Computing Systems, 2013 IEEE 33rd International Conference on Distributed Computing Systems 2007, pp. 54, doi:10.1109/ICDCS.2007.93