A rank sequence method for detecting black hole attack in ad hoc network

This paper discusses one of the route security problems called the black hole attack. In the network, we can capture some AODV route tables to gain a rank sequences by using the FP-Growth, which is a data association rule mining. We choose the rank sequences for detecting the malicious node because the rank sequences are not sensitive to the noise interfered. A suspicious set consists of nodes which are selected by whether the rank of a node is changed in the sequence. Then, we use the DE-Cusum to distinguish the black hole route and normal one in the suspicious set.

In this paper, the FP-Growth reflects an idea which is about reducing data dimensions. This algorithm excludes many normal nodes before the DE-Cusum detection because the normal node has a stable rank in a sequence. In the simulation, we use the NS2 to build a black hole attack scenario with 11 nodes. Simulation results show that the proposed algorithm can reduce much vain detection.