Abstract:
As the Internet services spread all over the world,
many kinds of security threats are increasing. Therefore,
existing Intrusion Detection Systems (IDS) facing very
serious issue for the Internet users for their day to day online
transactions, like Internet banking, online shopping, foreign
exchange and trading stocks. Genetic Algorithm is used to
identify various attacks on different type of connections. This
algorithm takes into consideration of different features in
network connections such as protocol type, duration, service,
to generate a classification rule set. Each rule set identifies a
specific type of attacks. A novel fuzzy class-association rule
mining method based on Genetic Network Programming
(GNP) is used for detecting such network intrusions. By
combining fuzzy set theory with GNP, the proposed method
can deal with KDDCup99 mixed dataset that contains both
discrete and continuous attributes. This method focuses on
building distribution of normal and intrusion accesses based
on fuzzy GNP. In an application of intrusion detection the
training dataset contains both normal connections and
several kinds of intrusion connections. GNP examines all the
tuples of the connections in the dataset to pick up the rules to
be stored in two independent rule pools; normal pool and
intrusion pool. Fitness function is defined, higher fitness of a
rule results in high Detection Rate (DR) and low Positive
False Rate (PFR), which means probability of intrusion is
high in the connection. By using this data can be classified
into normal class, intrusion class.
Keywords: Genetic Network
