A Study on Detection and Prevention of Blackhole & Greyhole Attacks in DTN

Abstract

Delay Tolerant Network is a network in which there is no end to end connectivity between source and destination. DTN is characterized by long propagation delay and intermittent connectivity. Due to the limited connectivity, DTN is vulnerable to various attacks, including Blackhole and Greyhole attacks. Malicious nodes drops all or a part of the received messages, even if they have enough buffer storage. This dropping behaviour is known as Blackhole and Greyhole attacks respectively. This paper provides a new scheme and its comparison with different parameters. Existing research scheme can detect individual attackers well but they cannot handle the case where attackers cooperate to avoid the detection. The limitation of previous work is that they cannot defend against collusion attacks. Therefore there is a need to develop new attack detection scheme that detects collusion attacks effectively. Blackhole and Greyhole behaviours represent a serious threat against routing in Delay or Disruption Tolerant Networks. Due to the unique network characteristics, designing a misbehaviour detection scheme in DTN represents a great challenge. DTN assume  that  network  nodes  voluntary  cooperate  in  order  to  work  properly. This cooperation is a cost intensive activity and some nodes can refuse to cooperate, leading to a selfish node behaviour. Normally nodes are required  to  exchange  their  encounter  data,  in  which  malicious  nodes  intentionally  drop  all  or  part  of  it. Thus, so the overall network performance could be seriously affected.  At  the  time  of  use  a watchdogs  is  a  well -known  mechanism  to  detect  selfish  nodes  like  that  trusted  authority, relying on local watchdogs alone can lead to poor performance when detecting selfish nodes, in term of precision and  speed. This  is  especially   important  on  networks  with  sporadic  contacts,  such  as  DTNs,  where  sometimes  watchdog’s   lack  of  enough  time  or  information  to  detect  the  selfish  nodes. Thus, we propose Statistical based Detection of Blackhole and Greyhole attackers (SDBG to address both individual and collusion attacks) to detect the individual and colluding misbehaviour nodes and prevent the network from them. Detection of attacker node increases the precision when detecting selfish nodes. Extensive simulation shows that our solution can work with various scenarios and different number of attackers per collusion at high accuracy and less delay.