Anomaly and misuse intrusion detection model : using neuro-fuzzy logic.
| dc.contributor.author | Kirwa, Solomon Cheruiyot | |
| dc.date.accessioned | 2011-07-22T07:21:44Z | |
| dc.date.available | 2011-07-22T07:21:44Z | |
| dc.date.issued | 2009 | |
| dc.description | Partial fulfillment for award of the degree of Master of Science in Information Technology. | en_US |
| dc.description.abstract | Intrusion detection systems are increasingly a key part of systems defense. Various approaches to intrusion detection are currently being used, but they are relatively ineffective. A major concern of existing anomaly intrusion detection approaches is that they tend to produce excessive false alarms. One reason for this is that the normal and abnormal behaviour of a monitored object can overlap or be very close to each other which makes it difficult to define a clear boundary between the two. This thesis presents a fuzzy logic model for misuse and access intrusion detection where instead of using crisp conditions, or fixed thresholds, fuzzy sets are used to represent the parameter space as defined by a human expert. This is implemented using a neuro-fuzzy system which is a high breed system combining | en_US |
| dc.identifier.citation | QA76.87.K57 2009 | en_US |
| dc.identifier.uri | http://hdl.handle.net/11071/1553 | |
| dc.language.iso | en | en_US |
| dc.publisher | Strathmore University | en_US |
| dc.subject | Security systems | en_US |
| dc.subject | Fuzzy logic | en_US |
| dc.subject | Intelligent Systems | en_US |
| dc.title | Anomaly and misuse intrusion detection model : using neuro-fuzzy logic. | en_US |
| dc.type | Thesis | en_US |