# Detection and Mitigation of RPL Rank and Version Number Attacks in the Internet of Things: SRPL-RP
## 5 Discussion
### Accuracy Discussion  
From the analysis of the results in Section 4, we found that SRPL-RP in the grid-random topology, SRPL-RP (Rank Attack) in the grid-random topology, and SRPL-RP (Version Number Attack) in the grid-center topology had the highest TN accuracy and the lowest FP accuracy among other topologies compared with SBIDS [13] and RPL+Shield [14]. This indicates that, in the case of TN, the percentages of the total number of malicious nodes that were correctly identified as attacking nodes were: 97.65%, 98.04%, and 98.04% for SRPL-RP, SRPL-RP (Rank Attack), and SRPL-RP (Version Number Attack), respectively. On the other hand, in the case of FP, the percentages of the total number of malicious nodes that were falsely identified as legitimate nodes were: 2.35%, 1.96%, and 1.89% for SRPL-RP, SRPL-RP (Rank Attack), and SRPL-RP (Version Number Attack), respectively. These are very small numbers compared with SBIDS [13], which had 7.04%, and RPL+Shield [14], which had 7.61%. We found that SRPL-RP in the grid-center topology, SRPL-RP (Rank Attack) in the grid-center topology, and SRPL-RP (Version Number Attack) in the random topology had the lowest FN accuracy and the highest TP accuracy among other topologies compared with SBIDS [13] and RPL+Shield [14]. This indicates that in FN, the percentages of the total number of legitimate nodes that were falsely identified as the malicious node were: 5.33%, 11.22%, and 1.35% for SRPL-RP, SRPL-RP (Rank Attack), and SRPL-RP (Version Number Attack), respectively. These are very small numbers compared with SBIDS [13] and RPL+Shield [14]. In TP, the percentages of the total number of legitimate nodes that were not influenced by the proposed protocol were: 94.67%, 88.78%, and 98.65% for SRPL-RP, SRPL-RP (Rank Attack), and SRPL-RP (Version Number Attack), respectively. We found that SRPL-RP in the grid-center topology, SRPL-RP (Rank Attack) in the grid-center topology, and SRPL-RP (Version Number Attack) in the random topology had the highest AR among other topologies compared with SBIDS [13] and RPL+Shield [14]. This indicates that the percentages of the total accuracy metrics were: 95.62%, 93.05%, and 98.30% for SRPL-RP, SRPL-RP (Rank Attack), and SRPL-RP (Version Number Attack), respectively, compared with SBIDS [13] and RPL+Shield [14]. The above analysis and discussion clarify that the proposed SRPL-RP was better in comparison with existing countermeasures in terms of the network performance and detection and mitigation accuracy. On the basis of the comparison in Table 15 of studies in the literature and the proposed SRPL-RP, we demonstrated that the proposed SRPL-RP can provide better functionalities, better network performance, and better detection accuracy. In addition, SRPL-RP supports against multiple attacks at the same time on the network. The effectiveness of the proposed SRPL-RP in terms of the network performance was better: in the grid-center topology for PDR, the best result obtained was 98.48%. In the random topology for the control message value, the best result obtained was 991 packets/s. In the random topology for average energy consumption, the best result obtained was 1231.778 joules. However, the effectiveness of the proposed SRPL-RP in terms of the accuracy was better: in the random topology of SRPL-RP (Version Number Attack) for AR, the best result obtained was 98.17% for the aforementioned reasons. The reason for this is that the proposed SRPL-RP can provide verification of the sender nodes by using the threshold. After the detection occurs, mitigation and isolation features can be applied to cope with the severe effects of both attacks. To achieve supplemental security, a blacklist table with a threshold alert was implemented to isolate and alert all other nodes to skip the malicious nodes from the RPL network. Therefore, the proposed SRPL-RP can assist in the development and in the reduction of the risks of RPL network security.