Dynamic anomaly by using incremental approximate pca in AODV-based MANETS

Mobile Ad-hoc Networks (MANETs) in contrast to other networks have more vulnerability because of having nature properties, such as dynamic topology and no infrastructure. Therefore, a considerable challenge for these networks, is a method expansion that can specify anomalies with high accuracy atnetwork dynamic topology alternation. In this paper, two methods were proposed for dynamic anomaly detection in MANETs, namely IPAD and IAPAD. The anomaly detection procedure consists of three mainphases: Training, detection and updating the two methods. In the IPAD method, to create the normal profile,we used the normal feature vectors and principal components analysis in the training phase. In detectionphase, during each time window, anomaly feature vectors based on their projection distance from the first global principal component specified. In updating phase, at end of each time window, normal profile updatedby using normal feature vectors in some previous time windows and increasing principal componentsanalysis. IAPAD is similar to IPAD method with a difference that each node use approximate first global principal component to specify anomaly feature vectors. In addition, normal profile will be updated by using approximate singular descriptions in some previous time windows. The simulation results using NS2simulator for some routing attacks show that an average detection rate and an average false alarm rate inIPAD method had 95.14% and 3.02% respectively. The IAPAD method had 94.20% and 2.84% respectively

Keywords:

MANETs , Dynamic Anomaly Detection , Routing attacks , Incremental Principal Component Analyses