Detecting Compounded Anomalous SNMP Situations Using Unsupervised Pattern Recognition
Fecha de publicación
Springer Science + Business Media
Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005. 15th International Conference, Warsaw, Poland, September 11-15, 2005. Proceedings, Part II. Lecture Notes in Computer Science. Volumen 3697, pp. 905-910.
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 whether such anomalous situations represent real intrusions.
978-3-540-28755-1 (Print) / 978-3-540-28756-8 (Online)
0302-9743 (Print) / 1611-3349 (Online)
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