# Performance Evaluation of Mobile RPL-Based IoT Networks under Hello Flood Attack
## 4 Related Works  
In this section, we explore several studies that have investigated the effects of the Hello Flood attack on wireless sensor networks (WSNs). These investigations have provided valuable insights into the behavior of WSNs under attack conditions. In the study conducted by [11], the impact of the Hello Flood attack was thoroughly examined by analyzing various performance metrics such as the Packet Delivery Ratio, received packets, dropped packets, and average throughput across different node counts. The analysis encompassed node counts ranging from 10 to 110, providing a comprehensive assessment of the attack's effects on network performance as node density varied. Based on simulations done with the network simulator NS2, the Hello Flood attack has a clear negative effect on wireless sensor networks, with overall network performance going down as the number of nodes increased. Furthermore, the authors in [12] delved into the mechanics of the flooding attack, detailing its creation and elucidating its adverse effects on network operations. The study used graphs to show how the attack directly affected throughput and End-to-End Delay. This helped researchers figure out how to find RREQ flooding attacks in WSNs. Additionally, the paper proposed a preventive measure to mitigate the detrimental effects of this attack, further contributing to the body of knowledge on network security in wireless sensor networks. The authors in [13] investigated the impact of the Hello Flood attack on packet delivery, received packets, projected packets, and overall network performance across varying node counts. Using the Network Simulator (NS2), they conducted simulations to analyze the effects of the attack on wireless sensor network (WSN) performance. Their findings unequivocally demonstrate a reduction in WSN performance during a flood attack, highlighting the disruptive nature of such attacks on network operations. Interestingly, the results also revealed a correlation between node count and network performance, indicating that increasing the number of nodes within the WSN can enhance overall network performance. This insight underscores the importance of considering node density when assessing network performance and security in WSN deployments. In their study outlined in [5], the authors investigated the impact of three distinct attacks on RPL networks: the Increase Number attack, the Hello Flood attack, and the Decrease Rank attack. Their analysis extended to scenarios involving multiple attackers and examined the effects over time, with the number of attacking nodes ranging from 1 to 3. The simulations revealed that these attacks could substantially disrupt network performance. It was interesting to see that the number of attackers had the most significant effect on End-to-End Delay (E2 ED), network throughput, and the Packet Delivery Ratio (PDR). This shows how important attack density is in determining how resilient and efficient a network is. In the study described in [14], researchers looked at the Hello Flood attack in a simulated environment. They compared how well the Low Energy Adaptive Clustering Hierarchy (LEACH) attack worked with the Hello Flood attack when there were 1 to 5 attacking nodes. The analysis was presented graphically, illustrating the percentage of the Packet Delivery Ratio (PDR) as a function of the number of attacking nodes in the network. The results revealed an exponential decline in the PDR with an increasing number of attacking nodes, underscoring the detrimental impact of heightened attack intensity on network performance. In another study [15], the impact of the Hello Flood attack was investigated across various scenarios, each comprising sink nodes, normal nodes, and malicious nodes. The introduction of an attacker node into each scenario allowed for the examination of network behavior under attack conditions. The number of nodes varied across scenarios, ranging from 5 to 25, enabling a comprehensive analysis of scalability.