Adaptive Intrusion Tolerant Database Systems
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Intrusion Tolerant Database System (ITDB) is a new paradigm for secure database systems that can detect intrusions, isolate attacks, contain damage, and assess and repair damage caused by intrusions. What makes ITDB superior to conventional secure approaches is that it has an ability to reconfigure. Thus, it can yield much more stabilized levels of trustworthiness under environmental changes. However, the reconfiguration faces the problem of finding the best system configuration out of a verylarge number of configuration sets and under multiple conflicting criteria, which is a NPhardproblem.Th...
Intrusion Tolerant Database System (ITDB) is a new
paradigm for secure database systems that can detect
intrusions, isolate attacks, contain damage, and
assess and repair damage caused by intrusions. What
makes ITDB superior to conventional secure
approaches is that it has an ability to reconfigure.
Thus, it can yield much more stabilized levels of
trustworthiness under environmental changes.
However, the reconfiguration faces the problem of
finding the best system configuration out of a very
large number of configuration sets and under
multiple conflicting criteria, which is a NPhard
problem.
This study focuses on two aspects of addressing
adaptation problems in ITDB. First, a rule-based
mechanism and neuro-fuzzy technique are proposed to
apply to the adaptation model. Second, this
study examines the effects of the rule-based
adaptive controller and the neuro-fuzzy adaptive
controller on the adaptation. The purpose of this is
to evaluate which of these techniques can yield
higher stabilized levels of trustworthiness, data
integrity, and data availability in the face of
attacks.
paradigm for secure database systems that can detect
intrusions, isolate attacks, contain damage, and
assess and repair damage caused by intrusions. What
makes ITDB superior to conventional secure
approaches is that it has an ability to reconfigure.
Thus, it can yield much more stabilized levels of
trustworthiness under environmental changes.
However, the reconfiguration faces the problem of
finding the best system configuration out of a very
large number of configuration sets and under
multiple conflicting criteria, which is a NPhard
problem.
This study focuses on two aspects of addressing
adaptation problems in ITDB. First, a rule-based
mechanism and neuro-fuzzy technique are proposed to
apply to the adaptation model. Second, this
study examines the effects of the rule-based
adaptive controller and the neuro-fuzzy adaptive
controller on the adaptation. The purpose of this is
to evaluate which of these techniques can yield
higher stabilized levels of trustworthiness, data
integrity, and data availability in the face of
attacks.
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