week13

In this week 13, I have decided to complete the chapter 4

“Discussion on effectiveness of identification and prevention methods”

“Recommendations”

In the Discussion on effectiveness of identification and prevention methods, ” I have described the methods and their effectiveness and importance

The effectiveness of each solution is studied for the recommendation of the best possible solution. Malicious nodes can be prevented in vehicular networks using a distributed prevention scheme. It is   a kind of effective method in the   in predicting DoS attack before it happens, therefore, it delivers high packet delivery ratio. Enhanced Attacked Packet Detection Algorithm (EAPDA)  is a highly responsive algorithm which delivers high throughput and low end-to-end delay. In mobility environment,

Fuzzy Petri Net model is effective in predicting Denial of Service attack for self-driving vehicles in external communication. This intrusion detection method is used for detection of malicious nodes in mobility environment. DDoS attacks can be also prevented in VANET using novel defence scheme as it identifies nodes causing congestion. Lastly, in this study, a real-time detection of random jamming and ON-OFF jamming is studied. It detects collision of beacons by mounting detector as sniffer on leading vehicle. The vehicle having detector mounted is leading vehicle, rest of vehicles then follow it.

the discussion was done in a best effective way after this

I moved to the further part namely recommendation

here in the recommendation I have suggested the  best identification and the preventive method

“EAPDA algorithm” can be recommended as a highly suitable detection method in which vehicle sends a message to RSU to obtain information related to traffic. This communication is done as per the allotted time slot and, RSU acknowledges messages from nodes by using timestamps. The time slot helps RSU in recording number of packets transferred to it from nodes, which further helps in deciding the unusual and normal behavior of nodes. Packets are identified and analyzed by RSU, if the number of packets increases and get double the rate than normal rate then, it is detected as a malicious node. When a malicious node is detected, the RSU removes the node from its database and does not communicate further. In this manner, this method can be helpful in identifying malicious nodes in vehicular networks. Also, sensors can be employed in traffic lights by which behavior of malicious nodes will be monitored and recorded.

the best prevention scheme that is used is

“Novel defense Scheme”. The RSU follows specific steps to protect VANET and prevent congestion. It continuously monitors all communications occurring between vehicles and vehicles to infrastructure to identify false information (traffic packets) generated by vehicles. The   RSU checks the information given by infrastructure and checks safety messages generated by vehicles. So, when a node sending false traffic packets is identified, RSU blocks all activities done by attacker or vehicle. In this manner, the   RSU manages the traffic caused by attacking node in VANET and prevents nodes from penetrating further.

So, to summarize, it can be said that although autonomous vehicles are gaining popularity in the automotive industry, much development is still needed to make this technology safe to use. More effective solutions are required to identify and prevent various kinds of attacks on vehicles and vehicular networks.

after the recommendation has done I have taken the feed back from my project supervisor Ali…….

he said me to add some more methods more I tried the level best…….

 

after completion of this part,

I have further moved to the final chapter that is chapter 5

I’m going to conclude there  Thank you…………….