Ensemble methods for anomaly detection and distributed intrusion detection in Mobile Ad-Hoc Networks

This paper examines the problem of distributed intrusion detection in Mobile Ad-Hoc Networks (MANETs), utilizing ensemble methods. A three-level hierarchical system for data collection, processing and transmission is described. Local IDSs (intrusion detection systems) are attached to each node of the MANET, collecting raw data of network operation, and computing a local anomaly index measuring the mismatch between the current node operation and a baseline of normal operation. Anomaly indexes from nodes belonging to a cluster are periodically transmitted to a cluster head, which averages the node indexes producing a cluster-level anomaly index. Cluster heads periodically transmit these cluster-level anomaly indexes to a manager which averages them.On the theoretical side, we show that averaging improves detection rates under very mild conditions concerning the distributions of the anomaly indexes of the normal class and the anomalous class. On the practical side, the paper describes clustering algorithms to update cluster centers and machine learning algorithms for computing the local anomaly indexes. The complete suite of algorithms was implemented and tested, under two types of MANET routing protocols and two types of attacks against the routing infrastructure. Performance evaluation was effected by determining the receiver operating characteristics (ROC) curves and the corresponding area under the ROC curve (AUC) metrics for various operational conditions. The overall results confirm the theoretical developments related with the benefits of averaging with detection accuracy improving as we move up in the node–cluster–manager hierarchy.