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Detection and Prevention of MAC Layer Misbehavior for Ad Hoc Networks
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MAC layer misbehavior in ad hoc networks - IEEE Conference Publication
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A novel scheme to prevent MAC layer misbehavior in IEEE 802.11 ad hoc networks
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Comparison of Seebeck and Peltier Effect. If the curves oscillate on either side of the mean and that the majority of the points are inside the limits. If a small number of points crossed the upper or lower control limit or deviate from the center line. The purpose is to identify which among this set of stations is a greedy one. Therefore, this detection scheme is implemented at every station to designate the cheater station through the supervision of the average reception throughput and the mean inter-packets time by control charts.
For the computation of the thresholds control chart parameters , we need a minimum of 20 values , but for the network monitoring, we draw every calculated value for the throughput and for the inter-packets time. This is the real-time detection that we highlight in our paper. The detection scheme is performed at any receiving node for every transmitting station as in Figure 4. In fact every node has the right to explore its received packets.
We can emphasize that one honest node in the state of transmission is sufficient to calculate the control chart parameters. The next section is dedicated to the performance analysis of the proposed detection scheme through NS-2 simulations. In our simulation parameters we used the shadowing model as a radio propagation model which is very near to the realistic radio propagation, taking into account the energy losses.
To achieve our detection method of the IEEE In our case we have chosen the simulator with the software platform and parameters depicted in Table 2. First, we calculated the control limits and center lines based on the results of the simulation in normal cases without IEEE In this case the two metrics throughput and inter-packets time are supervised in the control chart below composed by the control limits that we computed in the last section for a node in the network. As we can see in the control chart for throughput and the inter-packets time, curves oscillate on either side of the mean and the majority of the points are inside the limits.
Obviously we can decide that this node communicates in an environment without greedy attack. If few points come out of the control limits, we can explain this fact by the movement outside of the transmission range. Throughput moving range monitoring in normal case Inter-packets time moving range monitoring in normal case.
Monitoring in the MAC layer misbehavior case 5. First scenario detection of the attacked. In this monitoring case we note that when the throughput curve crossed the lower control limit and the inter-packets time curve crossed the upper control limit,. Consequently we can decide that this node is under a MAC layer misbehavior attack. We can also lay emphasis on the absence of any great change for the moving range curves related to the deviations for the mean but not for the amplitude, due to the greedy behavior.
In this monitoring case we reveal that the throughput curve crossed the upper control limit and the inter-packets time curve crossed the lower control limit. There is a strong deviation, so we can decide that this node is a greedy one this is the MAC layer misbehavior attack. We can also focus on a change in the moving range curve of the inter-packets time resulting from an improvement of the transmission time for the attacker due to the greedy behavior.
Inter-packets time moving range monitoring in attack case Figure 7 Control charts monitoring of the attacker. We plot the tolerance interval the difference between the upper and lower control limits as a function of the number of nodes. Our results are represented in the graphics below Figure 8.
Small and random variations in curves are detected. We should compute the chart parameters for every number of nodes to obtain a better supervision of the network. The throughput tolerance intervals depending on the number of nodes The inter-packets time tolerance intervals depending on the number of nodes.
The detection thresholds and the tolerance interval depend on the number of nodes; therefore, each receiver updates these parameters for each number of transmitters. In our work we tested the detection scheme in an ideal environment which depends on the number of nodes with constant bit rate traffic.
The statistical process control is a. Thus, the purpose is the separation of the extraordinary from the ordinary situations. The misbehavior at the MAC layer by changing the backoff mechanism can lead to performance degradation of the network. In this paper we tried to propose a novel detection scheme for this misbehavior based on the supervision of two metrics reception throughput and inter-packets time through statistical process control charts.
Our detection scheme presents several advantages. It does not require any changes in the IEEE Its most significant advantage is the detection of such attack in real time by visual graphs. In the perspective, we will try to extend the proposed scheme by introducing other performance measurements in order to develop other detection systems that are easier than the previous ones. We also plan an implementation of the so-called detection strategy in a realistic environment. Accessed July 10, Giri and N.
Princeton, NJ, Apr. Szott, M. Natkaniec, R. Canonico, and A. Bouras, S. Charalambides, M. Drakoulelis, G. Kioumourtzis, and K. Kyasanur, and N. Natkaniec, and A. Salleh, Z. Ishak, N. Din, and M. Paris, France: Edition d'Organisation, Introduction to Statistical Quality Control, 6th ed.
Bai, A. Rong, S. Lee, and H. Tippett, "On the extreme individuals and the range of samples taken from a normal population,'' Biometrika, v. Real-time detection of MAC layer misbehavior in mobile ad hoc networks Academic research paper on " Computer and information sciences ". The fourth section proposes our detection scheme of the IEEE IEEE This method requires that each station chooses a random waiting time between 0 and the size of a contention window CW value equals to a number of time slots , and expects the number of slots before transmission .
Figure 2 Backoff procedure 3. Related work The BEB algorithm provides a fair access to the medium. Proposed detection system 4. Modeling The misbehaving nodes choose a random backoff interval in the range of , where and W is the current contention window CW. Statistical process control The SPC ensures optimum quality based on statistical tools.
It aims to: - Give a tool to monitoring process. Indeed, two processes are never exactly similar. The Shewhart control chart measurements The Shewhart measurements shcuL process on the basi . The control chart for the individual measurements includes two graphs, the first is for individual value monitoring used for detecting the slip of the system and the second is for moving range used to monitoring the quality .
Detection strategy In this monitoring technique we propose supervising and plotting the average reception throughput and the mean inter-packets time by control charts Table 1. Table 2 Platform and parameters. Computation of control limits First, we calculated the control limits and center lines based on the results of the simulation in normal cases without IEEE Monitoring in normal case In this case the two metrics throughput and inter-packets time are supervised in the control chart below composed by the control limits that we computed in the last section for a node in the network.