Trust Mechanisms in MANETs

MANETs are a type of ad hoc networks that are made up of mobile devices that move around the network freely in any route they want without the existence of a central node like a base station or an access point, and connect to other nodes wirelessly using Wi-Fi, cellular, or satellite transmission to communicate with nodes that are within their range. MANETs don’t have a network infrastructure, they could belong to a group of mobile devices, or could connect to the Internet, figure 2.3. Because of their flexibility and limited physical security, MANETs are exposed to many security threats, nodes rely on their knowledge towards other nodes before they communicate with them.

Figure 2.3: Mobile ad hoc networks

Implementing proper trust models into MANETs is very important to describe the trustworthiness of nodes in the network, and to establish initial trust between nodes in the network. Existing trust methods used in MANETs include: Cryptography and

digital signatures [31, 37], probability [63], fuzzy logic [73], chain optimization and social networks [70].

G. Yajun et al. [31] presented a trust model that used basic trust followed with ap- plication trust. Basic trust formed as initial trust was established; the model allowed 2 nodes to exchange their credentials when they came in contact with each other for the first time. The credentials of any node in the network contained symmetrically en- crypted features about the node. These credentials were decrypted using a secret key that was exchanged between 2 nodes when they became in contact with each other. Nodes could choose which features in their credentials to share with other nodes. Once basic trust was formed, application trust formation was followed. Application trust was measured based on the node’s context, roles were assigned to nodes based on their application trust which changed when a node’s context changed. The model does not consider the mobility context of nodes.

S. Inoue et al. [37] presented a model that used certificates to evaluate the trust- worthiness of a trust value, and the node’s ID. The model had two stages, off-line and on-line. In the off-line stage, the certificate, called Attribute Certificate (AC), was is- sued by a node to its neighbour, the certificate contained the neighbour’s evaluated trust, and the issued AC was also stored in the issuing node as well. Every node in the network issued an AC for their neighbours. In the on-line stage, when a node wanted to send a message to a specific destination, it constructed paths to the destination. It then requested ACs from nodes that belonged to the paths it constructed, it validated the ACs. Using the validated ACs it measured the trust value of each route - paths with less hop counts had higher trust, and then chose the most trustworthy route to send its message. The method does not take advantage of spreading the trust value for nodes to increase their awareness of malicious nodes existence, if any.

B. Wang et al. [69], introduced a trust based Quality of Service (QoS) model. The model measured trust from direct and indirect trust. QoS ensured quality services in a route such as bandwidth, delay, and jitter. Usually the route was checked for its QoS before data was transmitted through it. The proposed model used delay only to estimate a route’s QoS. The expected transmission count was used as a metric to measure the quality of a route. To calculate direct trust, each node in the network assigned a trust value to its neighbour according to the neighbour’s ability to authen- tically forward packets. To calculate indirect trust, feedback from neighbours about a node was used. Each node stored a trust table that contained the trust value of ev- ery neighbour’s direct and indirect trust. When a source wanted to send a message to the destination, using the stored routing table, it searched for the possible routes that the message could be transmitted through. When a route was identified for data transmission, its trust value was first calculated using direct and indirect trust of each

node in the route. The route was also measured for its delay QoS level. If the accu- mulated trust value was high, and the delay QoS level was low, the route was chosen for data transmission. When a node was identified as malicious in a route, it was isolated from the network, thus making the route more secure. This model might not be feasibly applied to networks where delay is a natural feature such as DTNs and OppNets.

To safeguard the QoS of data availability, K. Bijon et al [63] proposed a prob- ability based model that adopted the Dempster-Shafer theory (reasoning with un- certainty) that competently collected recommendations from intermediate nodes and effectively discarded malicious ones. Trust values were assigned and stored in the in- termediate nodes of a path, the recommendations were prioritized based on the trust values of nodes in a path. Recommendations from nodes with a higher trust value were prioritized over recommendations from nodes with lower trust values. Rec- ommendations from shorter distanced nodes were given more priority over longer distanced nodes. The model also enhanced the trust values of nodes by measuring their ability to develop their trust, a node was given the choice to whether or not it wished to trust another node regardless of its recommendation value. This technique might not give fair results in a network with randomly mobile nodes.

To deal with uncertainty, a fuzzy recommendation based trust model for MANETs was introduced by J. Luo et al. [73]. Each node in the network monitored its neigh- bour’s packet forwarding patterns. Nodes recorded the results of their neighbour monitoring patterns into a table that contained the data forwarding information. Ev- ery time a node interacted with another node in the network it rated the interaction as either a positive or a negative one. Using the information recorded in the table, fuzzy direct trust was computed. Latest interactions were more valid than past interactions, but both were used to measure the trust value of a node. To build a trusted path, direct trust and feedback from other nodes towards nodes they interacted with were both used to calculate the fuzzy indirect trust with fuzzy properties. This model considers selfish attacks only and does not consider other attacks.

A model with trust chain optimization based on the stochastic Petri net technique (bipartite graph) and social networks was proposed by J. Cho et al. to measure the trust and social values of multiple nodes in a path [70]. When nodes evaluated each other’s trust they combined social trust with QoS trust to compute the total trust value of a node. Social trust was measured from direct and indirect trust derived socially from own experiences with other nodes, or the reputation of the node in the social network. QoS trust was measured from a node’s ability to provide good services and conduct positive interactions with other nodes in the network. When trust was measured in a path, its distance and number of nodes affected the computed trust,

where longer chains of nodes in a path weakened the trust value of a path. This technique relies on path length, which might not give accurate results for legitimate longer paths.

The available trust and reputation methods in MANETS are summarized in ta- ble 2.3.

Table 2.3: Trust and reputation methods used in MANETS Algorithm Objective Trust technique Trust property Trust method Metrics

[31] Trust establishment

Cryptography Basic trust (encrypted credential exchange) and application trust

(context)

Direct trust Results for model not shown [37] Secure routing Attribute

certificates

Attribute certificates requested and validated upon path construction

Direct trust Overhead (B), and delivery rate [69] Secure routing Trust based on

QoS

Secure path is chosen by the accumulated trust of all nodes in

the path

Direct and indirect trust

Delivery ratio, delay, routing packet overhead, and detection

ratio [63] Trust

management

Probability Dempster-Shafer theory. Recommendations from nodes with

a higher trust or from shorter distance nodes are given more

priority Direct recommended trust Time, average of recommendation hop lengths

[73] Secure routing Fuzzy logic Interactions are rated by nodes as either positive or negative, new

interactions are more valid

Direct and indirect trust

Packet forwarding ratio, energy consumption ratio, and

convergence speed [70] Secure routing Chain

optimization and social networks

Stochastic Petri net technique. Social trust is combined with QoS

trust to compute the total trust value of a node

Direct and indirect trust

Packet forwarding ratio, energy consumption ratio, and

convergence speed