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A wireless ad hoc network (WANET) is a self organizing system connecting the multiple nodes without any centralized access points. In WANET, nodes move randomly and transmit packets to other nodes within the network. Due to their random mobility, different intrusions are presented and thereby reducing the network lifetime. Therefore, Wireless ad-hoc networks need to be secured and utilize essential techniques to identify the misuse behaviors by detecting and classifying the normal and anomalous node. In order to improve the anomaly intrusion detection in WANET, Chi-Squared Distribution based Bootstrap Aggregating with SVM (CD-BASVM) classifier is introduced. The CD-BASVM classifier is used to detect the intrusion and classifying nodes as either normal or anomalous. At first, an optimal feature is selected from the dataset by using Chi-Squared Distribution for intrusion detection and classification. After that, bootstrap aggregating with an SVM classifier is employed to detect an intrusion and classify anomalous and normal nodes in a wireless ad-hoc network. This helps to obtain high anomaly intrusion detection accuracy. Simulation outcomes show that the CD-BASVM improves the performance with different parameters such as Classification time, false positive rate, End to end delay, data packet loss rate and anomaly intrusion detection accuracy compared to state-of-the-art methods.