Association Rule Pattern Mining Approaches Network Anomaly Detection

The research area for intrusion detection is becoming growth with new challenges of attack day by
day.  Intrusion  detection  system  includes  identifying  a  set  of  malicious  actions  that  compromise  the  integrity,
confidentiality,  and  availability  of  information  resources.  The  major  objective  of  this  paper  is  to  apply
association rule pattern mining approaches for network intrusion detection system. In this paper, traditional FP-
growth algorithm, one of the association algorithms is modified and used to mine itemsets from large database.
The required statistics from large databases are gathered into a smaller data structure (FP-tree). The itemsets
generated from FP-tree are used as profiles to check anomaly detection in the proposed system.