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Mobile ad hoc networks (MANETs) are multi-hop wireless networks of autonomous mobile nodes without any fixed infrastructure. In MANETs, it is difficult to detect malicious nodes because the network topology constantly changes due to node mobility. A malicious node can easily inject false routes into the network. A traditional method to detect such malicious nodes is to establish a base profile of normal network behavior and then identify a node's behavior to be anomalous if it deviates from the established profile. As the topology of a MANET constantly changes over time, the simple use of a static base profile is not efficient. In this paper, we propose a clustering-based anomaly detection approach, called DCAD, which allows the profile to be dynamically updated. In the approach, we use the weighted fixed width clustering (WFWC) algorithm in order to establish a normal profile and to detect anomalies. We also …