An experiment using factor graph for early attack detection
Cao, Phuong
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https://hdl.handle.net/2142/78579
Description
Title
An experiment using factor graph for early attack detection
Author(s)
Cao, Phuong
Issue Date
2015-01-23
Director of Research (if dissertation) or Advisor (if thesis)
Iyer, Ravishankar K.
Committee Member(s)
Iyer, Ravishankar K
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
probabilistic graphical models
factor graph
security incidents
preemptive intrusion detection
Abstract
This paper presents a factor graph based framework (named AttackTagger) for high
accuracy and preemptive detection of attacks. We use security logs of
real-incidents that occurred over a six-year period at the National Center for
Supercomputing Applications (NCSA) at the University of Illinois to evaluate
AttackTagger. Our data consist of attacks that led directly to the target system
being compromised, i.e., not detected in advance, either by the security
analysts or by intrusion detection systems. AttackTagger can detect 74 percent
of attacks before the system misuse. AttackTagger uncovered six hidden attacks
that were not detected by security analysts.
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