After the feedback, i have got from the ali i have done the modifications to the report I started reading the method “Fuzzy Petri Net model for predicting Denial of Service attacks for self-driving vehicles in external communication” .this was grasped from the artile “Prediction of DoS Attacks in External Communication for Self-driving Vehicles Using A Fuzzy Petri Net Model” written by the “Khattab M. Ali Alheeti, Anna Gruebler, Klaus D. McDonald-Maier, Anil Fernando” taken from the journal “2016 IEEE International Conference on Consumer Electronics (ICCE)”.
Fuzzy petri net model is a security system used for the protection of communication between vehicles in self-driving and semi self-driving vehicles. It is an effective intrusion detection method used for the detection of malicious nodes in a mobility environment. It is based on fuzzy petri nets successfully detecting DoS attacks and other packet dropping attacks in VANETs. Fuzzy petri nets are extracted from trace files of Network simulator – 2 (NS2).
In the mobility model two tools are used to generate an environment in real time for self-driving and semi self-driving vehicles: Simulation of Urban Mobility Model (SUMO) and Mobile Vehicles (MOVE)
1)Simulation of Urban Mobility Model (SUMO)
2) Mobile Vehicles (MOVE).
Intrusion detection systems using fuzzy Petri nets have some rules to classify normal and abnormal behavior of vehicles. there is also Another method under this intrusion detection system is Intelligent Security System. It is more effective in identifying vehicle’s malicious behavior through messages sent and received over the communication channel between a vehicle and the RSU. Security systems in vehicular networks have two primary concerns they are as folows
- Secure transmission of control packets or data packets from a vehicle to another point
- Secure transmission of all packets between vehicles present in that zoneThe system architecture of the FPN intrusion detection system is shown below. Fuzzy parameters are generated in the begining stage, Parameters get fuzzified and processed by fuzzy inference engine and the final output is generated. Inferencing engines use nine fuzzy rules to generate the output, which is compared with the threshold level to determine if a behavior is normal or abnormal.
After the analysis of this method was made then I have proceeded with the next method namely..
“preventing DDoS attack in VANET using a novel defense scheme”
this is the best practice to be done in order to avoid the DDoS attacks this was read from the article, the title of the article is “A Novel Defense Scheme against DDOS Attack in VANET” written by “Pathre, Ayonija; Agrawal, Chetan; Jain, Anurag”
In this method vehicles communicate with each other and the infrastructure to send and receive correct information about traffic from and to the RSU. But,
here when an attacker targets for an attack he starts to exploit the information and sends many false messages to the victim vehicle. the resources are exhausted causing the congestion in VANET.this scheme helps in detecting the misbehavior of nodes
When a conjunction occurs in VANET, nodes start to send congestion announcement signals to the neighboring nodes. These signals help in determining jams occurring in VANET so that nodes can take the best route.
RSU follows specific steps to protect VANET and prevent congestion. These steps are given below:
It continuously monitors all communications occurring between vehicles and vehicles to infrastructure
- It identifies false information (traffic packets) generated by vehicles
- It checks information given by infrastructure and checks safety messages generated by vehicles
- When a node sending false traffic packets is identified, RSU blocks all activities done by attacker or vehicle
- Manages the traffic caused by attacking node in VANET
I have shown the review done and grasped the concepts these were shown to the ali…..i got the feedback and did the modifications……………
After this, I have done the analysis of the method namely……..
“Detecting real-time DoS attacks in vehicular networks”
This technique is used in the detection of DoS attacks on IEEE 802.11 networks, The detector observes all the events that are happening in the wireless communication channel and computationally explains the occurrence of collisions in the network. here in this method, The real-time detector detects jams occurring in unicast traffic. It detects by considering beacons which are regularly transmitted in a broadcast mode of IEEE 802.11p networks.
. There are two models to be considered of DoS attacks: Random jamming and ON-OFF jamming. In random jamming, each transmitted packet is independently corrupted with a probability of k. In ON-OFF jamming, OFF state has no packet jamming, ON state undergoes damage of beacons with a probability of 1. After destroying beacons, the attacker goes to OFF jamming state. In this manner, ON-OFF transitions occur after a certain duration and beacons are destroyed with the probability of k.
after the better analysis of these methods, after grasping the whole concepts I have taken the feedback and done the modifications………
after successful completion of chapter 4….. I have moved to the chapter 4 ending part…………..
Hoping this information might be helpful to you
thank you………………………..
DEEPAK-UPPU 