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Module theory / Syzygy / Anomaly detection / Anomaly / Intrusion detection system / Type I and type II errors / Statistics / Data mining / Spam filtering


Community Epidemic Detection using Time-Correlated Anomalies Adam J. Oliner, Ashutosh V. Kulkarni, and Alex Aiken Stanford University� {oliner, ashutosh.kulkarni, aiken}@cs.stanford.edu
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Document Date: 2010-09-07 15:35:08


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File Size: 916,82 KB

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City

Anomaly / /

Company

Intel / /

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Facility

Alex Aiken Stanford University / /

IndustryTerm

security infrastructure / campus network / web servers / desktop applications / commodity web servers / web browsers / different client systems / web browser / heterogenous hardware / Alert-correlation systems / network-distributed software upgrade / file systems / on commodity server / detection algorithm / response tools / malicious software / client-server protocol / the-shelf software / zombie network / Apache web server / /

OperatingSystem

DoS / Linux / Fedora Core 6 / CentOS / /

Organization

National Science Foundation / Stanford University / /

Person

Ashutosh V. Kulkarni / Can Syzygy / Alex Aiken buffer / Adam J. Oliner / Ai / Alex Aiken / /

Position

judge / /

ProgrammingLanguage

PHP / HTML / /

SportsLeague

Stanford University / /

Technology

client-server protocol / RAM / PHP / 265 processors / Linux / antibodies / simulation / operating system / HTML / detection algorithm / web server / /

SocialTag