Intrusion detection in wireless networks has become an indispensable component of any useful wireless network security systems, and has recently gained attention in both research and industry communities due to widespread use of Wireless Local Area Networks (WLANs). This paper focuses on detecting intrusions or anomalous behaviors in WLANs with data clustering techniques. We first explore the security vulnerabilities of 802.11 or WI-FI networks and summarize the network traffic metrics that are important to model the security of wireless networks. Based on the metric studied we propose a clustering-based intrusion detection approach and evaluate it on a real-world large wireless network traffic dataset. The evaluation results demonstrate the eflectiveness of our proposed intrusion detection approach for wireless networks.