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Abstract
ABSTRACT In recent times, secured routing is a major research in MANETs. The behaviour of malicious nodes in this network increases the risk of threats and induces abnormal operations in MANETs. This affects the security of data transmitted between the nodes in the network. Hence, an effective technique is needed to prevent the abnormal nodes after the process of detection. In this paper, we propose an improved Trust Detection Algorithm to increase the probability of detection and prevention of Black Hole nodes in MANETs. The proposed framework observes the behaviour of each node using various trust metrics that includes the relationship between the sensor nodes, social and service attribute trust and QoS metric trusts. The behaviour of sensor nodes is found through the communication and mobility behaviour of each node. This method avoids the black hole nodes in MANETs, when the routing is carried out with Zone Routing Protocol (ZRP). Hence, the privacy of data is retained using the proposed method. The proposed method is tested in terms of different combinations of with and without trusts. The result shows that the proposed method is effective through various QoS metrics like overall throughput, packet loss, energy consumption, trust level, false acceptance rate and missed detection rate.
Highlights
The Mobile Adhoc Network (MANET) is a collection of wireless nodes, where each sensor nodes communicates with each other through access-points
The presence of malicious nodes in MANETs is considered as a serious security threat that largely affects the performance of network
Three different cases are used for testing the proposed trust model in MANET architecture that evaluates the reliability of a sensor node, which is given below:
Summary
The Mobile Adhoc Network (MANET) is a collection of wireless nodes, where each sensor nodes communicates with each other through access-points. The sensor node behaviour is considered to be more similar to human behaviour, where the interactions between any two nodes are nil and on other hand, such sensor node acquaints with other sensor node in case of better interaction based on trust level developed over a particular time instant [16] Such type of interaction between the sensor nodes show misbehaviour due to its non-participation of sensor nodes in routing that takes into consideration including dishonesty and energy constraints. The evolution of trust is carried out in all possible ways in a network and no additional metrics are required to evaluate the method This helps to estimate the trustworthy communication links to carry out the transmission between the sensor nodes without the presence of blackhole nodes.
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