Detecting Colluding Blackhole and Greyhole Attacks in Delay Tolerant Networks


Delay Tolerant Network (DTN) is developed to address intermittent connectivity and long delay in wireless networks. Thanks to the restricted connectivity, DTN is at risk of blackhole and greyhole attacks in which malicious nodes intentionally drop all or half of the received messages. Although existing proposals could accurately detect the attack launched by individuals, they fail to tackle the case that malicious nodes cooperate with every alternative to cheat the defense system. During this paper, we counsel a scheme called Statistical-based Detection of Blackhole and Greyhole attackers (SDBG) to deal with each individual and collusion attacks. Nodes are needed to exchange their encounter record histories, based mostly on which different nodes can evaluate their forwarding behaviors. To detect the individual misbehavior, we have a tendency to define forwarding ratio metrics that may distinguish the behavious of attackers from traditional nodes. Malicious nodes may avoid being detected by colluding to manipulate their forwarding ratio metrics. To continuously drop messages and promote the metrics at the identical time, attackers would like to make pretend encounter records frequently and with high forged numbers of sent messages. We exploit the abnormal pattern of appearance frequency and variety of sent messages in faux encounters to style a sturdy algorithm to detect colluding attackers. Extensive simulation shows that our resolution can work with numerous dropping possibilities and totally different variety of attackers per collusion at high accuracy and low false positive.

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