Design and Evaluation of an Anomaly Detection Method Based on Cross-Feature Analysis using Rough Sets for MANETs

With the proliferation of wireless devices, mobile ad-hoc networking (MANET) has become a very exciting and important technology. However, MANET is more vulnerable than wired networking. Existing security mechanisms designed for wired networks have to be redesigned in this new environment. In this paper, we discuss the problem of anomaly detection in MANET. The focus of our research is on techniques for automatically constructing anomaly detection models that are capable of detecting new or unseen attacks. We propose a new anomaly detection method for MANET. The proposed method performs cross-feature analysis on the basis of rough sets to capture the inter-feature correlation patterns in normal traffic. The performance of the proposed method is evaluated through a simulation. The results show that proposed method effectively detects anomalies and identify attack types using the intrusion detection method.