Visualization and Clustering for SNMP Intrusion Detection

Accurate intrusion detection still is an open challenge. Present work aims at being one step towards that purpose by studying the combination of clustering and visualization techniques. To do that, MOVICAB-IDS, previously proposed as a hybrid intelligent Intrusion Detection System (IDS) based on visualization techniques, is upgraded by adding automatic response thanks to clustering methods. To check the validity of the proposed clustering extension, it has been applied to the identification of different anomalous situations related to the SNMP network protocol by using real-life data sets. Different ways of applying neural projection and clustering techniques are studied in present work. Through the experimental validation it is shown that the proposed techniques could be compatible and consequently applied to a continuous network flow for intrusion detection.