An Iterative Algorithm for Trust Management and Adversary Detection for Delay-Tolerant Networks

Abstract

Abstract-Delay/Disruption Tolerant Networks (DTNs) have been identified as one of the key areas in the field of wireless communication, wherein sparseness and delay are particularly high. They are emerging as a promising technology in vehicular, planetary/interplanetary, military/tactical, disaster response, underwater and satellite networks. DTNs are characterized by large endto-end communication latency and the lack of end-to-end path from a source to its destination. These characteristics pose several challenges to the security of DTNs. Especially, Byzantine attacks in which one or more legitimate nodes have been compromised and fully controlled by the adversary can give serious damages to the network in terms of latency and data availability. Using reputationbased trust management systems is shown to be an effective way to handle the adversarial behavior in Mobile Ad hoc Networks (MANETs). However, because of the unique characteristics of DTNs, those traditional techniques do not apply to DTNs. Our main objective in this paper is to develop a robust trust mechanism and an efficient and low cost malicious node detection technique for DTNs. Inspired by our recent results on reputation management for online systems and e-commerce, we develop an iterative malicious node detection mechanism for DTNs referred as ITRM. The proposed scheme is a graph-based iterative algorithm motivated by the prior success of message passing techniques for decoding low-density parity-check codes over bipartite graphs. Applying ITRM to DTNs for various mobility models, we observed that the proposed iterative reputation management scheme is far more effective than well-known reputation management techniques such as the Bayesian framework and EigenTrust. Further, we concluded that the proposed scheme provides high data availability and packet-delivery ratio with low latency in DTNs under various adversary attacks which attempt to both undermine the trust and detection scheme and the packet delivery protocol.

A Security and Efficient Routing Scheme with Misbehavior Detection in Delay-Tolerant Networks

“…Therefore, we adopt vector and node meet, they exchange the trusted routing tables TRT , TRT with each other. After the update operation, as shown in Table 3, the trusted routing tables TRT , TRT of node and node contain five row vectors send trusted routing table TRT to node ; (8) receive trusted routing table TRT from node ; (9) end (10) while ∀ ∈ TRT ∩ TRT do (11) if TRT ⋅ ⋅ TwID > TRT ⋅ ⋅ TwID then (12) verify the validity of the signature Sig ; (13) if Sig is valid then (14) update row vector in trusted routing table TRT ; (15) TRT ⋅ = TRT ⋅ ; (16) TRT ⋅ ⋅ TwID = TRT ⋅ ⋅ TwID; (17) TRT ⋅ Sig = TRT ⋅ Sig ; (18) end ( Robustness Analysis. In the building and updating process of the trusted routing table, the malicious node may modify the trusted routing table to forge high trust value and message forwarding ratio.…”

“…, ); (10) if ⋅ > ⋅ then (11) get copy number nc from head field of ; (12) calculate nc using Eq. (8); (13) forwards a copy of with copy number nc to node ; (14) updates nc ← nc − nc ; (15) else (16) if ⋅ == 0 and ⋅ == 0 then (17) calculate , of node and node ; (18) if == 0 and == 0 then (19) forwards a copy of to node ; (20) else…”

“…Zhu et al [7] exploit a trusted authority (TA) to judge the forwarding behavior of nodes based on the collected delegation task evidence, forwarding history evidence, and contact history evidence. Chen et al [13], Lu et al [15], Ayday and Fekri [18], Li and Cao [14], Zhao et al [19], and Chen and Chan [20] adopt the incentive mechanism to motivate selfish nodes to forward messages, which use reputation or credit to represent the forwarding behavior of nodes.…”

Dynamic Trust Management for Delay Tolerant Networks and Its Application to Secure Routing

“…To the best of our knowledge, only [2,3,11,13,24] used feedback mechanisms or indirect recommendations for trust management. In addition, [2,3] used discrepancies of indirect recommendations for adversary detection. Not leveraging direct-observation based trust/reputation deriving from social networking is a main drawback of these approaches.…”

“…A limitation of their work is that no consideration was given to inside attackers. [2,3] designed an iterative trust management scheme for DTNs. They used authentication as the underlying mechanism to evaluate a node.…”