A preliminary two-stage alarm correlation and filtering system using SOM neural network and K-means algorithm
Intrusion Detection Systems (IDSs) play a vital role in the overall security infrastructure. Although the IDS has become an essential part of corporate network infrastructure, the art of detecting intrusion is still far from perfect. A significant problem is that of false alarms, as generating a huge volume of such alarms could render the system inefficient. In this paper, we propose a new method to reduce the number of false alarms. We develop a two-stage classification system using a SOM neural network and K-means algorithm to correlate the related alerts and to further classify the alerts into classes of true and false alarms. Preliminary experiments show that our approach effectively reduces all superfluous and noisy alerts, which often contribute to more than 50% of false alarms generated by a common IDS.
alarm correlation intrusion detection system false alarm self organising map (som) k-means clustering