week14

In this week 14, I have done the conclusion part I have made the conclusion stating all the objectives are met with the related work

The preference of people is shifting from manual vehicles to self-driving vehicles, due to dissatisfaction with the safety and liability of manually driven vehicles. As navigation is done without the help of human beings, they are called driverless vehicles or self-driving vehicles. Autonomous vehicles provide a safe and secure navigation. They can improve the quality of life. But, this technology is prone to attacks. This study focused on DoS and DDoS attacks on autonomous vehicles and vehicular networks. In this study investigation of various kinds of DoS and DDoS attacks like DoS attack on vehicle to vehicle communication, overwhelming of node resources, jamming the channel in extended level of Denial of service attack, DoS attack on vehicle to infrastructure communication has done   along with the tabular description of type of attack and its impact. In order to identify   and prevent the DoS and DDoS attacks, Analysis of various methods namely distributed prevention scheme, enhanced attacked packet detection algorithm(EAPDA), Fuzzy petri net model for prediction, novel defense scheme, detecting real-time DoS attacks in vehicular networks has been provided with a tabular explanation along with strength, weakness and application of those methods.  Discussion on the effectiveness of Identification and prevention methods has done by focusing on each method. In this study, to identify and prevent the DoS and DDoS attacks the best methods namely Enhanced attacked packet detection algorithm (EAPDA) and Novel Defence scheme were recommended with their best explanation.

. Several DoS attack techniques are used for the purpose of establishing attacks. These techniques help in disabling services by exhausting the resources required to offer services. Many network and host based attacks are launched and these cause a lot of damage to a  network. DoS attacks are carried out on V2V and V2I scenarios, which cause immense damage to network and vehicles are not able to access vehicular network services. To detect malicious nodes and then prevent them, many detection and prevention schemes have been proposed and studied in this report. The best and most suitable identification and prevention schemes have been recommended. But, as AV technology is being used and development is on the rise, security against attacks is needed. The vulnerabilities of vehicles and vehicular networks should be studied and the impact of attacks should be known. In this way, attacks can be predicted before they get launched. Future work can be done by simulating different scenarios and testing them in real-life

After all the modifications were made I have shown to my project supervisor Ali and got the feedback

I have made all the modifications to the report like headings, spacing adding the title page and the declaration

 

I have checked the plagiarism and submitted…..up to now everything went well thank you………………..

thank you………………..