Abstract: This study presents behavior-based wireless network intrusion detection using genetic algorithm that assumes misbehavior identification by observing a deviation from normal or expected behavior of wireless node. The feature set is constructed from MAC layer to profile the normal behavior of wireless node. The wireless node behavior is learned by using genetic algorithm and current wireless node behavior can be predicted by genetic algorithm based on the past behavior. A 3-tuple value i.e., entropy index, newness index, mismatch index is calculated for constructed feature set in a session. The 3-tuple value of a wireless node behavior in a session are compared with expected non-intrusive behavior 3-tuple value to find intrusions. The performance of wireless intrusion detection is evaluated using detection probability and false alarm probability.