## 5 Simulation and Results
### Results  
We implement a black hole attack in the RPL protocol, which is a critical threat to IoT networks, by utilizing the Contiki operating system within the Cooja simulator. This investigation occurs within the 6 LoWPAN IoT environment, focusing on the execution and analysis of black hole attacks to understand their implications thoroughly. Moreover, our study introduces and assesses dedicated detection and prevention algorithms tailored to counter black hole attacks within RPL-based IoT systems. This research serves to demonstrate the effectiveness of our approach in strengthening the detection and mitigation of such malicious activities in the realm of IoT security. Similar to TTD, we selected 50 nodes for our simulations. TTD considers belief, disbelief, and uncertainty as parameters to distinguish between malicious and benign nodes. In contrast, our proposed ORM is based on honesty, dishonesty, energy, unselfishness, similarity, and neighborhood trust. Figure 4 compares the number of true positives correctly recognized by both approaches as the number of intruder nodes increases up to 15 in the network. The ORM's superior performance over the TTD and RWC approaches across various metrics is notably evident in the simulation results. When analyzing true positives (TPs), the ORM demonstrates better accuracy, particularly when confronted with scenarios involving 1, 3, 11, 13, and 15 malicious nodes. These instances highlight the ORM's efficiency in pinpointing malicious nodes accurately. Conversely, instances with 5, 7, or 9 intruder nodes show comparable performance between both methodologies. However, the ORM shows enhanced accuracy as the number of malicious nodes increases, indicating a consistent performance over the TTD method, as depicted in Figure 4. The ORM's multifaceted trust calculation involving energy, unselfishness, honesty, dishonesty, and similarity metrics likely enables it to detect a node integrity more precisely than TTD, as reflected by higher accuracy in detecting intruders when the number of malicious nodes varies. Additionally, the results obtained with the RWC method show similar trends to the ORM, with RWC consistently outperforming TTD in terms of true positives across different scenarios, though not surpassing the ORM's performance. Figure 5 depicts the scenarios where benign nodes are misclassified as malicious. The ORM consistently outperforms TTD across various intrusion scenarios, showcasing its enhanced ability to minimize false positives. This is due to the ORM's delta-based trust isolation, allowing it to precisely pinpoint and isolate suspicious nodes, thereby reducing the misclassification of benign nodes as malicious entities. Additionally, the ORM's comprehensive trust evaluation, incorporating multiple metrics, likely contributes to its more refined identification of benign nodes, further minimizing false positives compared to TTD. In comparison to TTD and ORM methods, the RWC method exhibits a notably higher incidence of false positives across varying scenarios with different numbers of intruder nodes. This tendency suggests a potential limitation in RWC's ability to accurately distinguish between benign and malicious nodes, resulting in a higher rate of misclassifying benign nodes as intruders. While TTD and ORM methods demonstrate more consistent performance, with generally lower false positive rates, RWC's elevated false positive occurrences underscore its inferiority in intrusion detection within IoT environments. While both TTD and ORM methods exhibit good accuracy for true positives and false positives, RWC's notably higher false positive rate makes it less suitable for intrusion detection tasks in IoT environments. Given the substantial discrepancy in false positive rates and the consistent performance of TTD in true positives and false positives, subsequent analyses will focus solely on comparing the ORM with TTD. This selective comparison  
Figure 4: Comparative analysis of true positive recognition in ORM vs. TTD and RWC.