# Analysing the Performance of a Trust-Based AODV in the Presence of a Flooding Attack
## 2 Related Work
There are three transmission rate threshold values and each category has its own threshold value. When the source node receives an RREQ packet from a neighbouring node, it first checks its type and compares its transmission rate to the threshold value of its category. If the transmission rate is below the threshold value for the category, the RREP packet is processed; otherwise, the packet will be discarded and the node will be added to the blacklist. [PERSON] et al. [25] proposed a scheme that allows a limited number of RREQ packets to be sent by any source node in the network. The limit was set at 10 RREQ packets per second. This low rate helped prevent flooding the network with RREQ packets. The disadvantage of this approach is that the malicious node can change the value of the allowed rate parameter because it has full access to it. [PERSON] and [PERSON] [26] provided a hybrid approach for preventing black-hole and grey-hole attacks. The proposed algorithm uses a hash function and timestamp base solution. The source node selects the second shortest route to the destination instead of the first. The probability of the existence of a black-hole attacker decreases for the second shortest route. This is because black-hole and grey-hole attackers send RREP packets to the source stating that they have the shortest route to the destination. Choosing the second shortest path is thus safer in the AODV protocol. The point of monitoring the RREP packets coming from the neighbouring nodes and ignoring the one with the shortest path is a direct trust mechanism for improving MANET performance. [PERSON] et al. [27] suggested a recommendation exchange protocol (REP), which enables a node to exchange trust values with its neighbouring node. In this model, the trust values are calculated based on direct observation. The decision to isolate a specific node is made using the evidence collected individually and by recommendations received from neighbouring nodes. Each node in the network assigns a trust value to its neighbours of between 0 and 1. This model consists of two phases: the learning phase and the trust phase. The learning phase is responsible for collecting data and converting it into knowledge. The trust phase is responsible for using that knowledge to detect and isolate malicious nodes. [PERSON] et al. [28] defined a trust management system that provides a degree of assurance for the future behaviour of nodes based on the services previously received from the nodes. [PERSON] et al. classified the trust and reputation management system into two major categories, namely individual-level trust and system-level trust. The individual-level trust mechanism allows the source node to initialise communication with the subject node, aggregate declarations from other nodes about prospective communications, evaluate the trustworthiness of potential interaction based on the past recorded data and make a trustworthiness decision on whether to interact with the subject node. The system-level trust mechanism concentrates on applying punishment based on the node's trustworthiness and reputation to improve the utility for nodes that are highly trustworthy. [PERSON] et al. [29] suggested adding two new packets to the AODV protocol: a route confirmation reply (CREP) packet and a route confirmation request (CREQ) packet. When an intermediate node receives an RREQ packet from the source node, it checks its routing table and sends an RREP packet back to the source and sends a CREQ packet to its next hop node towards the destination node. When the next-hop node receives the CREQ packet, it sends a CREP packet back to the source node if it has a fresh route to the destination node. When the source node receives the CREP and RREP packets, it validates the path to the destination by comparing the paths in the two packets. If both agree, the path is valid. [PERSON] et al. [30] proposed a solution requiring additional computation which results in an increased overhead.