Detecting Blackhole Attack on AODV-based Mobile Ad Hoc Networks by Dynamic Learning Method

This paper analyzes the blackhole attack which is one of the possible attacks in ad hoc networks. In a blackhole attack, a malicious node impersonates a destination node by sending a spoofed route reply packet to a source node that initiates a route discovery. By doing this, the malicious node can deprive the traffic from the source node. In order to prevent this kind of attack, it is crucial to detect the abnormality occurs during the attack. In conventional schemes, anomaly detection is achieved by defining the normal state from static training data. However, in mobile ad hoc networks where the network topology dynamically changes, such static training method could not be used efficiently. In this paper, we propose an anomaly detection scheme using dynamic training method in which the training data is updated at regular time intervals. The simulation results show the effectiveness of our scheme compared with conventional scheme.