## 2 Related Work
In a study by [PERSON] et al. [18], a mitigation procedure was suggested for isolating black hole nodes in the network. In this technique, all neighboring nodes report the suspicious node to the root of the network. Subsequently, it becomes the responsibility of the root node to scrutinize the routines or activities of the suspicious node. If the suspicious node is confirmed as malicious, the root instructs all neighboring nodes to cease communication with that particular node. A drawback of this technique is the extended time required before the compromised node is effectively isolated. [PERSON] et al. [1] proposed an exponential smoothing-based approach, wherein the activities of all nodes are recorded. The objective of recording these activities is to identify the last time a specific node transmitted a packet to the root. In the end, the root examines whether any node surpasses the predefined time. If so, it is concluded that the node is malicious. However, a weakness of this approach lies in the potential for a significant number of false positives. For instance, a node experiencing communication or other issues may be incorrectly identified as malicious. In [10], the authors presented a technique named Encrypted Data Packets, which utilized different encryption mechanisms. A significant drawback of this method is that it increases the packet response time, as all packets are encrypted in this process. [PERSON] et al. proposed a scheme named the Robust Trust Model (RTM) [19], which enhances the security of RPL by identifying the most secure path in the network. However, a weakness of this technique is its high energy consumption, leading to a degradation in network performance. Furthermore, the reporting mechanism employed in RTM is not suitable, resulting in a higher occurrence of false positives. [PERSON] et al. proposed a technique for selecting the best parent node using the trust-based threshold mechanism against rank modification attacks [8]. The advantage of this technique is the early isolation of any malicious node during parent node selection, effectively mitigating rank attacks. However, a limitation of this method is its ineffectiveness in addressing other routing attacks, such as the black hole attack. [PERSON] et al. proposed a theoretical methodology for quantitatively measuring trust based on two axioms [20]. The first is referred to as the entropy-based model, while the second is named the probability-based model. Both models fulfill their respective axioms. The advantage of this technique is that these two models can enhance the network's throughput. However, a challenge associated with these models is the extensive computation required to satisfy the axioms. [PERSON] et al. [21] classified the determination of trust metrics in a network into being elicited through a direct trust or an indirect trust technique. A trust-based model called Logistic Trust is proposed in [22]. It is established based on the source node's communication experience and the recommendations provided by the neighboring nodes of the target node. Logistic Trust employs a function known as the Logistic Function (LF), which integrates both direct and indirect trust to identify malicious nodes in the network. The main issue with this technique is that both the calculated direct and indirect trusts are compared to a predefined threshold value. If any node's packet loss ratio exceeds this threshold, then that node is considered malicious. However, there could be various reasons for a poor packet loss ratio that are not indicative of the node being compromised. In [23], the authors proposed a dynamic trust model based on a time decay factor. The method calculates the trust value of a node considering various factors, such as interaction, attribute similarity, public friend count (the number of mutual friends), and the time factor, with particular emphasis on the time factor, which significantly influences the trust value of the nodes. When a node lacks interaction with other nodes in the network, its trust value decreases.