Intrusion Detection System using K2 Self Learning Algorithm and Open Attacking Plateform

The goal of a this IDS is to identify malicious behaviour that targets a network or a host and its resources. Intrusion detection parameters are numerous and in many cases they present uncertain and imprecise causal relationships which can affect attack types. A Bayesian Network here used is a graphical modeling tool which used to model decision problems containing uncertainty. BN and K2 learning along with open attacking system is used here to make an automatic self-learning intrusion detection system based on signature recognition. But here is the goal to detect not only signature of attack also identifying the new pattern of new attack and storing its signature to database. Also here a host based IDS attached to backside of the network based IDS to provide security not only from outside but also from insiders. Keywords—Intrusion Detection; IDS; Network Security; Bayesian Network; K2 Learning; Network Based; Host Based; Anomaly; Hacking.