[talks] Reminder: 1:30pm Thu Nov 1 talk on anomaly detection in CS 402

Jennifer Rexford jrex at CS.Princeton.EDU
Wed Oct 31 14:23:41 EDT 2007

SPEAKER: Nina Taft, Intel Research
TITLE: Anomaly Detection in Large Networks using Approximation Techniques
DATE/TIME: 1:30-2:30pm Thu Nov 1
PLACE: room 402 in the CS Building


A tremendous enthusiasm for amassing enormous amounts of network
measurement data has spurred the development of numerous applications
that incorporate data mining techniques. In this talk we question the
hidden assumption in these applications that one needs to collect "all
the data all the time".  We consider this question in the context of
an anomaly detection application. We study the popular "Subspace
method detector" that is based on PCA analysis.  This method normally
collects data from many parts of the network, centralizes the data,
and then analyzes it to uncover anomalies. In our research, we ask
whether we can throw away some of the data. Can we still do anomaly
detection accurately without all the data?

To avoid backhauling large amounts of data through networks, we
present a framework that couples filtering at local monitors with
centralized detectors that can operate on approximate views of the
global data (i.e. network state). We show that the errors made by the
central detector - due to the use of approximate data - can be upper
bounded using matrix perturbation theory. The challenge is to design
the filtering parameters; these are determined by the bound on
detection errors and the criteria being tracked for detection. Our
approximate anomaly detector can detect anomalies with 80 to 90% less
data than the original method, and incurs less than a 1% reduction in
detection accuracy. Finally, we comment on issues and future directions
for data reduction in the context of anomaly detection.


Nina Taft is currently a senior research scientist at Intel Research
Berkeley. At Intel she works focuses on enterprise network traffic
characterization, anomaly detection at both the enterprise and host
and approximation techniques for data mining algorithms. Prior to
joining Intel, Nina worked at Sprint for 5 years in the IP Group working
on backbone Internet measurement. She conducted research in various
areas such as traffic matrix estimation, traffic characterization,
routing protocols and
IP-over-WDM network design problems. Prior to Sprint, Nina worked at SRI
International for four years. There her work focused on congestion
control and QoS routing in ATM networks. She received her PhD degree
from the University of Berkeley in 1994. Nina is currently serving as an
associate editor for the IEEE Transactions on Networking (ToN) journal,
was SIGCOMM 2007 PC co-Chair, and is a member of the ACM Internet
Measurement Conference (IMC) steering committee.

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