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...

• Type: Conference
• Year: 2005
• Where: ICANN
• Authors: Emilio Corchado, Álvaro Herrero, José Manuel Sáiz
• Keywords: Anomalous Network Behavior, Anomalous Snmp, ICANN 2005, Intrusion Detection