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ICANN
2005
Springer

Detecting Compounded Anomalous SNMP Situations Using Cooperative Unsupervised Pattern Recognition

8 years 8 months ago
Detecting Compounded Anomalous SNMP Situations Using Cooperative Unsupervised Pattern Recognition
This research employs unsupervised pattern recognition to approach the thorny issue of detecting anomalous network behavior. It applies a connectionist model to identify user behavior patterns and successfully demonstrates that such models respond well to the demands and dynamic features of the problem. It illustrates the effectiveness of neural networks in the field of Intrusion Detection (ID) by exploiting their strong points: recognition, classification and generalization. Its main novelty lies in its connectionist architecture, which up until the present has never been applied to Intrusion Detection Systems (IDS) and network security. The IDS presented in this research is used to analyse network traffic in order to detect anomalous SNMP (Simple Network Management Protocol) traffic patterns. The results also show that the system is capable of detecting independent and compounded anomalous SNMP situations. It is therefore of great assistance to network administrators in deciding whet...
Emilio Corchado, Álvaro Herrero, José
Added 27 Jun 2010
Updated 27 Jun 2010
Type Conference
Year 2005
Where ICANN
Authors Emilio Corchado, Álvaro Herrero, José Manuel Sáiz
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