An Integrated Approach Using Self Organizing Maps And Fuzzy Cognitive Maps For Network Intrusion Detection
The basic function of anomaly-based sensors is to detect any deviation from normal system behavior. However, clear merits between normal and abnormal patterns are very difficult to realize in practice especially when new systems are added or removed from the system network dynamically. A typica...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2009
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| Subjects: | |
| Online Access: | http://eprints.usm.my/53043/1/tesis%20an%20integrated%20approach%20using%20self%20cut.pdf |
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| Summary: | The basic function of anomaly-based sensors is to detect any deviation from
normal system behavior. However, clear merits between normal and abnormal
patterns are very difficult to realize in practice especially when new systems are
added or removed from the system network dynamically.
A typical problem that arises when deploying intrusion detection sensors is
their affinities of producing high rate of false alerts. Thus, it needs huge analysis
efforts and time consuming odd jobs at higher levels, The main purpose 0fthis thesis
is to propose a new soft computing inference engine model for intrusion detection. In
this study, we have investigated an approach to anomaly intrusion detection based on
causal knowledge reasoning. The approach is anomaly-based and utilizes causal
knowledge inference based fuzzy cognitive maps (FCM) and self organizing maps
(SOM). |
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