Detecting blackhole and greyhole attacks in vehicular Delay Tolerant Networks

Blackhole and greyhole attacks can cause severe problems in Delay- and Disruption-Tolerant Networks (DTNs), where connectivity is intermittent and long delays are actually the norm. Traditional security protocols cannot completely address such problems in DTNs, hence an efficient algorithm to detect malicious nodes in DTNs is imperative. In this paper, we propose a misbehavior detection system to defend against blackhole and greyhole attacks. By collecting and securely exchanging data of previous encounters, a node can assess the trustworthiness of other nodes in order to detect blackhole and greyhole attacks. We evaluate our method through extensive simulations using different DTN routing protocols. Our simulation results show that even when the drop probability of greyhole attacks varies in a wide range, our approach can still efficiently detect evil nodes with a high detection rate and a low false positive rate while maintaining a low energy consumption.