# Intelligent Intrusion Detection of Grey Hole and Rushing Attacks in Self-Driving Vehicular Networks
## 6 Discussion  
The core objective of this research was to create an IDS that provides a secure environment for self-driving and semi self-driving vehicles. We implemented the methodology of this IDS in eight phases, which lead to generating the mobility and traffic model, the trace file, the ns-2, fuzzification, data collection and pre-processing, training and testing for the SVM, and training and testing for the FFNN, and compared the results we generated from the two types of IDS. In Figure 7, two IDS are compared; we found that the IDS based on the FFNN was more effective and efficient in determining \begin{table}
\begin{tabular}{c c c} \hline \hline
**Performance Metrics** & **Without Response System** & **With Response System** \\ \hline Generated Packets & 8226 & 8226 \\ Received Packets & 3555 & 6216 \\ Packet Delivery Ratio & 43.21\% & 75.56\% \\ Totally Dropped Packets & 4878 & 2010 \\ Average End-to-End Delay & 72.90 ms & 44.65 ms \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Performance metrics of abnormal behaviour. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class** & **PR** & **DR** & **CR** & **MSE** \\ \hline FFNN-IDS & 99.76\% & 99.83\% & 99.89\% & 0.167\% \\ SVM-IDS & 99.90\% & 99.84\% & 99.79\% & 0.150\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Performance metrics. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Performance Metrics** & **Without Response System** & **With Response System** \\ \hline Generated Packets & 8226 & 8226 \\ Received Packets & 5769 & 8153 \\ Packet Delivery Ratio & 70.13\% & 99.11\% \\ Totally Dropped Packets & 3074 & 74 \\ Average End-to-End Delay & 202.17 ms & 22.63 ms \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Performance metrics of normal behaviour. abnormal vehicles with a smaller false negative alarm rate than the IDS using SVM; however, we can observe that SVM performance is much higher that of the FFNN. SVM is faster than FFNN because the SVM automatically computes the number of hidden layers in an optimised way [46]. Figure 7 also shows the difference in the performance of FFNN and SVM. The IDS system based on SVM had an error rate of 0.19%. However, in this particular system, there was fluctuation of alarm rates between 99.92% and 99.70% with high rate and efficient accuracy. Alternatively, the average false negative rate of alarm recorded was low, around 0.20%, which is a good indicator of the performance of the results. The rate of error for the IDS based on the FFNN was found to be 0.17% and the fluctuation of the alarm rate was between 99.92% and 99.75% with a high and efficient rate of accuracy. Alternatively, the average false negative rate of alarm was minimal, at about 0.1. Making use of fuzzified data with FFNN and SVM create flexibility in selecting the system that is very efficient with various conditions, thus allowing improving the rate of detection. Additionally, 15 significant features were incorporated, such as payload ID, MAC Ethernet, IP, Packet Tagged and Hop Counts, into the IDS. Thus, the features selected help in making the proposed security system more efficient in securing the external communication systems of self-driving and semi self-driving vehicles. In the experimental result of the response mechanism protocol phase, we note the efficient role of the safe mode protocol in providing and establishing the safety environment for self-driving and semi self-driving vehicles. This protocol can adapt with these vehicles under different conditions, i.e., normal and abnormal behaviours. It has the ability to isolate the communication of the infected vehicles from the surrounding mobility and immobility nodes but keep connection with the closest RSU to introduce this victim vehicle in the safe mode communication. In order to evaluate the performance of the IDS, we need to compare it with the previous best achieved average error rate, which is 2.05% [39], while we have achieved an 0.18% error rate with the IDS presented here.