week11

In this week 11th, I have studied and analyzed  the

“A Distributed Prevention Scheme from Malicious Nodes in VANETs’ Routing Protocols” written by “Tarek Bouali, Hichem Sedjelmaci, Sidi-Mohammed Senouci”

the journal name is “IEEE Wireless Communications and Networking Conference (WCNC 2016) – Track 4 – Services, Applications, and Business”

sometimes a node which has a legal certificate and is considered authenticated may also launch an attack. So, knowing the behavior of a node is an important factor in detecting and preventing DoS attack on vehicular environments. It is an intrusion prevention and detection scheme that decides behavior of nodes whether malicious or normal, based on a  Kalman filter

 

This scheme works based on clustered architecture called as cluster head (CH) monitors vehicles. It monitors the vehicles and based on their behavior, it classifies them as white, black or gray. Other nodes present in the network send their recommendations to CH to help it calculate the trustworthiness of nodes. Also, CH calculates trust based on its experience with them and from recommendations given by monitoring agents. Future behavior is calculated using a  Kalman filter and nodes are classified into white, black or gray.

these are the classification of the vehicles as stated

In this article, The author implemented an intrusion prevention scheme using simulator NS3.17 for proving the effectiveness and the accuracy in results.

various parameters were considered like end-to-end, packet delivery ratio and detection rate. Monitoring architecture is comprised of three phases; bootstrapping phase, CH election phase, and CH maintenance phase. In the bootstrapping phase, a node is assigned a trust value by its neighboring nodes, This is done by continuously gathering information about other nodes and continuously listening to traffic.

 

The phase time is pre-fixed and the optimal values are then calculated. In the  CH election phase, the trust values of vehicles are known to the other vehicles in the vehicular network. Each node in the network creates a message based on the known trust value; (CHEAD_MESSAGE(IP Address, Trust Value), The  IP address of the  neighbor with the highest trust value is introduced and is then diffused in the  network. When this message is received, node compares the trust value with the trust value in its own created message. If trust value comes to be greater than its trust value, it changes its IP address and CH value is replaced with new information. If not, the node simply ignores the message and does not alter the contents of its message. In the third and final phase; CH maintenance, CH assesses maintenance of cluster before leaving the cluster. CH has the responsibility of continuously calculating distances between vehicles after a certain period. When vehicles go beyond a threshold level, a new CH is assigned. So, CH sends the address of the node which has the highest trust value to every other member present in the cluster. Then, cluster members update their IP address and store the trust level of the newly calculated trust level of the node.

When the phases are over and they mark the completion of the prediction process, CH proceeds with the classification of vehicles based on trust level calculations. Three lists are created; white list, gray list, and black list. If a vehicle or node is deemed to be highly trusted and can very well support VANET applications safely, then it is classified as a white list member. If a vehicle cannot be trusted highly, in other words, it is a weak node and can be trusted only a little, it can only be used in support of VANET applications when no white node exists. Such node is classified as a gray list member but, gray list nodes have a tendency to change their behavior in the future. Finally, as the name suggests, black list members are the ones that are considered malicious and should not be used in any applications.

I have read this method and summarized. Then after I have read the

“Detecting Denial of Service attack in vehicular environments using Enhanced Attacked Packet Detection Algorithm (EAPDA)” 

“A novel mechanism for detecting DOS Attack in VANET using Enhanced Attacked Packet Detection Algorithm (EAPDA)”  written by Priya Sharma, Amarpreet Singh taken from the journal “Proceedings of 2015 RAECS UIET Panjab University Chandigarh”

In this an algorithm  was proposed to detect malicious nodes using a verification check methodology.

here RSU’s do the verification process like knowing the  location of vehicle, vehicle’s ID, timestamp and much more. So, this algorithm focuses Vehicle to Road Side Unit communication (V2R) with the help of  packets referred to as control packets.communication is done in an allotted timeslot and the RSU acknowledges messages from nodes by using time stamps,

The time slot helps the  RSU in recording the  number of packets transferred to it from nodes, which further helps in deciding unusual and normal behaviour of nodes.

Packets are identified and analysed by the  RSU. If the number of packets increases and reaches double the rate of the  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

This is one of the best methods for preventing the malicious nodes………………………………………………..

then after I have met my project supervisor for the feed back of the summary content…………………………………………………………………………………………..

this was the main concepts that I have understood from the articles…………………

hoping this might be helpful to all…..who are in need……….

then i moved to do the study on further analysis………on week 12 th………..

THANK YOU………………………..