# Robust and Secure Data Transmission Using Artificial Intelligence Techniques in Ad-Hoc Networks
## 1 Introduction  
The Internet of Things (IoT) is an innovative technology that allows physical things such as homes, vehicles, hospitals, and many more to be integrated with the digital world through an internet connection [1]. The demand and the use of IoT techniques have grown significantly over the last couple of years. Using this technique, physical things are made smarter and have found application in smart buildings [2], transportation [3], healthcare systems [4], and many more. IoT gathers data from physical things, stores it and thencommunicates it to different networks. Here, we considered IoT in the Mobile Ad-Hoc Network (MANET).  
In MANET, the nodes are movable and any nodes are able to be processed as a host as well as a router within the network [5]. Therefore, MANET is more vulnerable to security attacks including active and passive attacks in the network. MANET comprises mobile/portable wireless devices that communicate without the requirements of fixed infrastructure. Since the nodes can be moved freely, the network topology is dynamic and can easily leave and join the network when required [6]. In MANET, nodes can easily communicate with different nodes that are lying in the range of communication. As shown in Figure 1, N3 and N2 are in the communication range of node N1. Therefore, N1 can forward its data to the N3 and N2 nodes directly [7]. These nodes are known as the neighbor nodes. The node that is not connected is directed to the communicating node (Source node) and a route is formed by other nearby nodes. The data transmission in MANET takes place in a safe and forward manner where each node acts as a router. The address of the destination node is discovered without any additional methods. Further, a distinguishing work in the field of intrusion detection was also presented by [PERSON] et al. [8; 9].  
Inside the MANET, every node is treated as a router as well as a host. A collection of rules, known as protocols, is established to move data between different nodes inside the network. The protocols in MANET may be classified into two types: proactive and reactive routing protocols [10], with proactive routing procedures deciding on route construction based on the features of the previously stored node d in the routing table, also known as table-driven routing protocols. In reactive routing, on the other hand, the creation of the route is done on demand [11]. For route generation in this study, we employed the Ad-hoc On-Demand Distance Vector (AODV) routing protocol. Section 3 provides a more extensive description.  
The network has vulnerability to multiple Service Denial (DoS) attacks, such as black holes, gray holes, sinkholes, and many more. For this reason, the design and implementation of such a network are based on the following assumptions: inside the network, nodes are depending on each other, however some nodes are irresponsible. These undesired nodes degrade network performance whenever they get a chance. In the Black Hole (BHA) node, the complete data traffic is attracted by the malicious nodes by sending the highest hope count towards the source node. As per assumptions of the source node, the node with a higher sequence number is genuine and forwards the entire data [12].  
Several researchers have used different techniques either individually or in combination with other approaches to minimize the effect of the Black Hole Attack (BHA) in MANET. These techniques include security IOT [13], sequence number of thresholds [14], acknowledgment [15], cross-checking [16], trust [17], cross-layer [18], and clustering [19], including others. The simulation was performed using the MATLAB simulation tool in which the detection of black hole node was performed using a modified AODV protocol, which is designed by integrating optimization with a Machine Learning (ML) approach.