# Resistance to Cybersecurity Attacks in a Novel Network for Autonomous Vehicles
## 10 Discussion  
Running the scenario at different total times also allowed some insights into how overall time impacts certain metrics. From the data gathered during this experiment, it can be seen that the longer the attack time, the higher the chance of delivery, the higher the latency, and for Spray and Wait the lower the buffer time. The investigation of the effect of an increasing overall attack time on the buffer time of individual nodes looks like an interesting area for further research because, if using the mean, an increase in time indicated an increase in the buffer time. However, if using the median, in the data from this experiment, it was the other way around, as the total attack time increases, the median buffer time decreased.  
An interesting observation that can be made is that the delivery probability for the MaxProp and PRoPHET algorithm always increased between 10-15 black holes and then Maxprop significantly dropped again between 25-30. When comparing this to the latency graphs, it can also be seen that the latency was a direct opposite; it reduced between 10-15 and then rose again at 25-30. This could be because the black holes were clearing the network of some of the traffic. If the black holes were discarding messages that had been delivered but were still in the network, it was beneficial since as the traffic cleared, the messages traveled faster and were therefore more likely to be delivered. This affected the more methodical protocols, as for Spray and Wait the messages were replicated so many times (during the spray phase) that there was always an excess of messages travelling through the network.  
The difference between the effect of the two types of attacking nodes (grey and black holes) was mostly seen in the delivery probability. Black-hole nodes confidently disrupted the network and reduced the delivery probability for the Maxprop and Spray and Wait algorithms. From the results collected it appeared that for all of the protocols, the introduction of grey-hole nodes increased the probability of a message being delivered. This is probably most likely due to the fact that as messages were dropped, traffic in the network was cleared. Given that it was only dropping half of the messages, it is also likely that as messages are being replicated a copy of the message was still traversing the network and would be delivered by a different node. This means that grey holes could be used by attackers to gather data from the messages without being as detectable.  
The most surprising difference between the two type of attacking nodes was in the buffer time graphs. It was here that for the Spray and Wait algorithm, the trend line changed from quadratic to linear. This was not the expectation as, given the nodes perform the same function, just at half the rate, it would be a valid assumption that the rate would be different by a factor of two rather than a factor of x.  
To enhance the research further, the attacking nodes could introduce some vector alterations in order to trick the other nodes into sending more data. By increasing their own vector, other nodes think that they are the best route to the destination node. This is amore realistic and complex method of trying to disrupt a network, as that way the black hole nodes receive and drop more data.