  This paper proposes an intrusion detection and prediction system based on
uncertain and imprecise inference networks and its implementation. Giving a
historic of sessions, it is about proposing a method of supervised learning
doubled of a classifier permitting to extract the necessary knowledge in order
to identify the presence or not of an intrusion in a session and in the
positive case to recognize its type and to predict the possible intrusions that
will follow it. The proposed system takes into account the uncertainty and
imprecision that can affect the statistical data of the historic. The
systematic utilization of an unique probability distribution to represent this
type of knowledge supposes a too rich subjective information and risk to be in
part arbitrary. One of the first objectives of this work was therefore to
permit the consistency between the manner of which we represent information and
information which we really dispose.
