Anomaly Detection in Mobile Adhoc Networks (manet) Using C4.5 Clustering Algorithm

Mobile adhoc networks, abbreviated as MANETs are more vulnerable to various attacks than other types of networks. This is because of natural properties of MANETs such as dynamic topology, ever changing infrastructure and lack of central controller. Hence detecting anomaly behaviours in MANETs is a challenging task and this topic has attracted many researchers. In this paper, clustering method based anomaly detection method is proposed. The method uses C4.5 clustering algorithm to classify the events as normal or abnormal. The proposed method works in three phases viz. training, detection of anomaly behaviour and identifying the type of attack. The proposed method uses 141 features of network to identify the attacks. The experiments were conducted on NS2 simulator and results show that the proposed method is effective in detecting anomalies. Moreover, False Alarm Ratio (FAR) is also low which ensure that the detected anomalies are real attacks.