2026-09-19T10:43:49Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/283992022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Peter Szolovits.
Hastings, Joseph R., 1980-
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2005-09-26T20:14:24Z
2005-09-26T20:14:24Z
2003
2004
http://hdl.handle.net/1721.1/28399
56979320
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, February 2004.
Includes bibliographical references (leaves 131-133).
This thesis describes an attempt to monitor patterns of system calls generated by a Unix host in order to detect potential intrusion attacks. Sequences of system calls generated by privileged processes are analyzed using incremental Bayesian segmentation in order to detect anomalous activity. Theoretical analysis of various aspects of the algorithm and empirical analysis of performance on synthetic data sets are used to tune the algorithm for use as an Intrusion Detection System.
by Joseph R. Hastings.
M.Eng.
133 leaves
6708034 bytes
6724616 bytes
application/pdf
application/pdf
en_US
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Incremental Bayesian segmentation for intrusion detection
Thesis
application/pdf
false
Publication
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="9bfd14c1-3861-41d4-be8b-e6e5bb8655a4">
<Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type>
<Language>en_US</Language>
<Title>Incremental Bayesian segmentation for intrusion detection</Title>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2004</PublicationDate>
<Authors>
<Author>
<DisplayName>Hastings, Joseph R., 1980-</DisplayName>
<Affiliation>
<OrgUnit>
</OrgUnit>
</Affiliation>
</Author>
</Authors>
<Editors>
</Editors>
<Publishers>
<Publisher>
<DisplayName>Massachusetts Institute of Technology</DisplayName>
<OrgUnit />
</Publisher>
</Publishers>
<License>http://dspace.mit.edu/handle/1721.1/7582</License>
<Keyword>Electrical Engineering and Computer Science.</Keyword>
<Abstract>This thesis describes an attempt to monitor patterns of system calls generated by a Unix host in order to detect potential intrusion attacks. Sequences of system calls generated by privileged processes are analyzed using incremental Bayesian segmentation in order to detect anomalous activity. Theoretical analysis of various aspects of the algorithm and empirical analysis of performance on synthetic data sets are used to tune the algorithm for use as an Intrusion Detection System.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>