Detecting blackhole and greyhole attacks in vehicular Delay Tolerant Networks

Guo, Yinghui; Schildt, Sebastian; Wolf, Lars

doi:10.1109/comsnets.2013.6465569

Cited by 28 publications

(26 citation statements)

References 9 publications

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“…SDBG achieves a detection rate of at least 70% with false positive rate below 1.6%. Compared to state-ofthe-art schemes [14], [15], SDBG can detect collusion attack with higher accuracy and lower false positive rate.…”

Section: Introductionmentioning

confidence: 92%

Detecting Colluding Blackhole and Greyhole Attacks in Delay Tolerant Networks

Pham

Yeo

2016

IEEE Trans. on Mobile Comput.

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Delay Tolerant Network (DTN) is developed to cope with intermittent connectivity and long delay in wireless networks. Due to the limited connectivity, DTN is vulnerable to blackhole and greyhole attacks in which malicious nodes intentionally drop all or part 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 each other to cheat the defense system. In this paper, we suggest a scheme called Statistical-based Detection of Blackhole and Greyhole attackers (SDBG) to address both individual and collusion attacks. Nodes are required to exchange their encounter record histories, based on which other nodes can evaluate their forwarding behaviors. To detect the individual misbehavior, we define forwarding ratio metrics that can distinguish the behavious of attackers from normal nodes. Malicious nodes might avoid being detected by colluding to manipulate their forwarding ratio metrics. To continuously drop messages and promote the metrics at the same time, attackers need to create fake encounter records frequently and with high forged numbers of sent messages. We exploit the abnormal pattern of appearance frequency and number of sent messages in fake encounters to design a robust algorithm to detect colluding attackers. Extensive simulation shows that our solution can work with various dropping probabilities and different number of attackers per collusion at high accuracy and low false positive.

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Section: Introductionmentioning

confidence: 92%

Detecting Colluding Blackhole and Greyhole Attacks in Delay Tolerant Networks

Pham

Yeo

2016

IEEE Trans. on Mobile Comput.

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“…They first submit their tickets and then reveal their contact history to one another. Similarly, Guo et al [24,25] proposed Misbehavior Detection System (MDS) that helps in finding malicious nodes in vehicular DTNs. They also used the concept of encounter tickets similar to [23].…”

Section: Related Workmentioning

confidence: 97%

“…They also used the concept of encounter tickets similar to [23]. The schemes [23][24][25] detect the presence of malicious nodes in the network. However, these schemes would not be able to detect the isolator from the network because isolator neither drops nor forwards other nodes messages.…”

Section: Related Workmentioning

confidence: 99%

DISIDE: Distributed strategy identification in opportunistic mobile networks

Pal

Misra

2015

Computer Communications

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“…Due to such unusual network behavior, DTN uses store-carry-forward model as message propagation process. In store-carry-forward model, DTN node will send message to intermediate nodes only if it gets an opportunity or only if it comes in range of other node else it elect to save the node in its buffer [1]. Also there may be possibility of long run persistent buffer storage.…”

Section: Introductionmentioning

confidence: 99%

A Co-operative Mechanism to Contrast Black-hole Attacks in Delay Tolerant Networks

Upadhyaya¹

2016

IOSR

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Detecting blackhole and greyhole attacks in vehicular Delay Tolerant Networks

Cited by 28 publications

References 9 publications

Detecting Colluding Blackhole and Greyhole Attacks in Delay Tolerant Networks

Detecting Colluding Blackhole and Greyhole Attacks in Delay Tolerant Networks

DISIDE: Distributed strategy identification in opportunistic mobile networks

A Co-operative Mechanism to Contrast Black-hole Attacks in Delay Tolerant Networks

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