  In this paper we report our experiment concerning new attacks detection by a
neural network-based Intrusion Detection System. What is crucial for this topic
is the adaptation of the neural network that is already in use to correct
classification of a new "normal traffic" and of an attack representation not
presented during the network training process. When it comes to the new attack
it should also be easy to obtain vectors to test and to retrain the neural
classifier. We describe the proposal of an algorithm and a distributed IDS
architecture that could achieve the goals mentioned above.
