Jamming Detection Attack On VANET -- 3
Analyzes Packet Delivery Ratio (PDR), signal strength and carrier sensing time, throughput, load, data dropped are parameters to determine if a network is being jammed.
In this paper we are analyzing the performance of Vehicular ad hoc networks under jamming attack to propose a mechanism approach based on metric parameters to detect jamming attack to be implanted in OPNET. threshold based technique has been proposed in which node which is consuming power data than the threshold value will be responsible to trigger jamming attack we perform extensive simulations and analysis to show how significant such strategically placed attacks can be compared to random placement of limited-range jammers, The performance of network is measured with respect to the QoS parameters and Throughput, (PDR), signal strength, carrier sensing time.
How to improve threshold based technique proposed in which node which is consuming power data than the threshold value will be responsible to trigger jamming attack combines a considerable set of metrics and automatically selects appropriate thresholds?
In this work, we analyze twelve performance metrics in VANET to study their relationships, and the impact they cause on the energy consumption of the network.
There are parameters that by themselves cause an increase in the energy consumed by a node.
For example, retransmissions represent high energy costs in the node because it involves waiting times, extra packets sent and received by the node, searching and processing in the node’s routing tables, and listening to the communications channel.
One of this study’s significant contributions is to analyze these implications to define an adequate model for detecting possible threats to the network.
the presence of jamming because the VANET begins to behave differently, acquiring readings altered to normal, thus showing the influence of each type of jamming with the behavior of each of the performance metrics.
In this work, these anomalies in the network performance metrics are referred as symptoms.
The symptoms are manifestations that indicate that the state of health of a VANET is not in normal conditions, such as a very low level of PDR, or a very high level of energy consumed.
In Table 3, we present some of these relationships between jamming and metrics.
The variations of increase, decrease or oscillation of the metrics are with respect to the values of these present in the steady state.
It is observed that constant and deceptive jammers in general cause a greater decrease in the PDR and a greater increase in the negative value of the RSSI.
As there are fewer packets received and less signal strength, the search for routes with better quality becomes greater, consuming more time and energy in processing the information for forwarding.
The relation between the above metrics are used in many ways to detect and mitigate jamming attacks.
If, for example, the routing table of a node increases considerably, its processor must work more and consume more energy, having less time to react to any contingency.
The basic simulation setup has different scenarios comprising of 6 at least vehicle nodes representing vehicles moving at a constant speed of 5 meters per seconds.
There are different simulation scenarios; showing number of jammers in each scenario as well as the jammer transmitter power levels
Impact of
Normal
Jamming
Dual jamming
Triple jamming
On
PDR
Throughout
Média access delay
Energy consuming
RSSI
Average Energy Consumption of the network for non-jammed, a
jammer, two jammers, anti-jamming with one jammer, and antijamming with two jammers.……
Packet loss
Using AODV protocol routing
The algorithm begins by requesting as inputs the network scheme, the number of nodes and the area, taking into account that the counters of the four types of jamming and no jamming are at zero.
Next, a database provides the data tables obtained with the simulations and the experiment.