  Nowadays Intrusion Detection System (IDS) which is increasingly a key element
of system security is used to identify the malicious activities in a computer
system or network. There are different approaches being employed in intrusion
detection systems, but unluckily each of the technique so far is not entirely
ideal. The prediction process may produce false alarms in many anomaly based
intrusion detection systems. With the concept of fuzzy logic, the false alarm
rate in establishing intrusive activities can be reduced. A set of efficient
fuzzy rules can be used to define the normal and abnormal behaviors in a
computer network. Therefore some strategy is needed for best promising security
to monitor the anomalous behavior in computer network. In this paper I present
a few research papers regarding the foundations of intrusion detection systems,
the methodologies and good fuzzy classifiers using genetic algorithm which are
the focus of current development efforts and the solution of the problem of
Intrusion Detection System to offer a realworld view of intrusion detection.
Ultimately, a discussion of the upcoming technologies and various methodologies
which promise to improve the capability of computer systems to detect
intrusions is offered.
