  Mobile ad hoc networking (MANET) has become an exciting and important
technology in recent years because of the rapid proliferation of wireless
devices. MANETs are highly vulnerable to attacks due to the open medium,
dynamically changing network topology and lack of centralized monitoring point.
It is important to search new architecture and mechanisms to protect the
wireless networks and mobile computing application. IDS analyze the network
activities by means of audit data and use patterns of well-known attacks or
normal profile to detect potential attacks. There are two methods to analyze:
misuse detection and anomaly detection. Misuse detection is not effective
against unknown attacks and therefore, anomaly detection method is used. In
this approach, the audit data is collected from each mobile node after
simulating the attack and compared with the normal behavior of the system. If
there is any deviation from normal behavior then the event is considered as an
attack. Some of the features of collected audit data may be redundant or
contribute little to the detection process. So it is essential to select the
important features to increase the detection rate. This paper focuses on
implementing two feature selection methods namely, markov blanket discovery and
genetic algorithm. In genetic algorithm, bayesian network is constructed over
the collected features and fitness function is calculated. Based on the fitness
value the features are selected. Markov blanket discovery also uses bayesian
network and the features are selected depending on the minimum description
length. During the evaluation phase, the performances of both approaches are
compared based on detection rate and false alarm rate.
