| Document Date: 2006-10-19 19:27:44 Open Document File Size: 252,75 KBShare Result on Facebook
City Dirichlet / / Company America Online / Norton / AOL / INTEL / Microsoft / / / Facility By building / Columbia University / / IndustryTerm detection systems / anomaly detection algorithms / anomaly detection algorithm / intrusion detection systems / few possible solutions / security solution / probabilistic anomaly detection algorithm / data mining / malicious software / literature detailing alternative algorithms / malicious software exploits / prevention systems / host-based security systems / native logging tools / anomaly detection systems / learning-based anomaly detection algorithm / virus detection algorithms / prior algorithms / signature algorithms / unsupervised cluster-based algorithms / detection algorithms / vulnerable systems / learning-based anomaly detection algorithms / using the native logging tools / / OperatingSystem Microsoft Windows / Windows NT / / Organization Columbia University / Department of Computer Science / / Person Frank Apap / Eleazar Eskin / Salvatore J. Stolfo / Shlomo Hershkop / Andrew Honig / Katherine Heller / / Position general model for any host-based anomaly detector / messenger / Singer / representative / Instant Messenger / / Technology LAN / Anomaly detection algorithms / second anomaly detection algorithm / unsupervised cluster-based algorithms / the PAD and OCSVM algorithms / virus detection algorithms / literature detailing alternative algorithms / PHAD algorithm / network computer system / machine learning / operating system / operating systems / learning-based anomaly detection algorithm / anomaly detection algorithm / 4.2 PAD Anomaly Detection Algorithm / OCSVM algorithm / One algorithm / data mining / two anomaly detection algorithms / two learning-based anomaly detection algorithms / probabilistic anomaly detection algorithm / network protocols / signature algorithms / /
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