Purchase Instant Access. If you have access to this article please login to view the article or kindly login to purchase the article. Forgot password? Don't have an account? Procedia Comput. Radosavac and J. Baras, Detection and prevention of MAC layer misbehavior in ad hoc networks. Chiejina, E. Xiao and B. Christianson, A dynamic reputation management system for mobile Ad Hoc networks. Computers, 4: Zhang, F. Nait-Abdesselam and J. Murphy, Gaur and V. Laxmi, Open Comput. Rajabi, M. Taheri and M. Naderi, Sekar, A fault tolerance data aggregation scheme for wireless sensor networks.

Software, 8: Bagchi, Stealthy attacks in wireless ad hoc networks: Detection and countermeasure.

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IEEE Trans. Mobile Comput. Khan, Low complexity signed response based sybil attack detection mechanism in wireless sensor networks. Agrawal and S. Silakari, Satapathy, M. Sanyal and P. Sarkar Eds. Kozma, Jr. Lazos, Reactive identification of misbehavior in ad hoc networks based on random audits. Mangla, A.

Giuli, K. Lai and M.

Penalization of Selfish and Misbehaving Nodes at MAC Layer in Mobile Ad Hoc Networks

Baker, Mitigating routing misbehavior in mobile ad hoc networks. Nadeem, A. Howarth, Ad Hoc Netw. Sutaone, A cluster based routing protocol with mobility prediction for mobile sensor networks. Selvaraj, Shakshuki, E. Kang and T. Sheltami, Biswas and S. Karmakar, Intrusion detection in mobile Ad-hoc networks: Bayesian game formulation.

Rangaswamy, An efficient cross-layer based intrusion detection system for mobile Ad Hoc networks.


Applied Inform. Lazos and W. Kozma, The validation of all obtained results is performed in the network simulator NS2. Please cite this article as: Aaroud, A. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form.

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  4. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. The goal of this misbehavior is handling the protocol to increase the greedy nodes transmission rate at the expense of the other honest nodes. One of the most significant advantages of the IEEE However sharing the transmission channel makes the networks vulnerable to several attacks such as jamming, black holes, greedy behavior MAC layer misbehavior [12].

    By this channel-access misbehavior a greedy node can benefit from several advantages such as:. Our paper is organized as follows. The second section is dedicated to presenting the architecture of the IEEE An overview of the research works related to the IEEE The fourth section proposes.


    In the fifth section, the authors evaluate the performance of their approach using the NS2 simulator. Conclusions and perspectives are presented in the last section. The IEEE Before transmitting, a node first listens to the shared medium such as listening for wireless signals in a wireless network to determine whether another node is transmitting or not. This method requires that each station chooses a random waiting time between 0.

    The BEB algorithm provides a fair access to the medium.

    A novel scheme to prevent MAC layer misbehavior in IEEE 802.11 ad hoc networks

    Greedy nodes change their BEB to increase their throughput at the expense of other honest nodes. This greedy behavior is considered as misbehavior of the IEEE Several approaches have been proposed in the literature for the detection of the IEEE Tiwary [16] proposed a detection scheme based on the statistical collection of all nodes RTS retransmission due to time out, packet retransmission due to ACK timeout and throughput at receiver, then compared these results with the threshold values to decide whether a selfish attack is occurring.

    This method does not require any changes in protocols but it creates computation overhead. Other authors [17] also proposed an extension to the The main idea is to let both the sender and receiver agree on a random value of backoff through a public exchange using an engagement method inspired by the protocol of applying flipping coins over the telephone. However, it is still unable to detect collusion between sender and receiver. An approach of greedy nodes detection in IEEE This idea results from the strong linear correlation noticed between nodes in terms of transmission instants.

    This method uses a modular architecture which comprises individual tests and a decision making component DMC. We propose in the following section a new detection strategy based on a statistical quality control approach statistical process control. We use the Shewhart chart for individual value, applied to the receiving throughput and the average time between receptions.

    Our new detection strategy can be implemented on any receiving node to monitor the network in real time. As we will demonstrate by the simulation of the proposed detection scheme does not require modifications of the IEEE To the best of our knowledge our approach based on statistical process control has not been proposed before in the literature to detect greedy behavior in mobile ad hoc networks.

    The stochastic process is defined as follows:. The probability that a node in the network transmits a packet in a randomly chosen slot is denoted as. Its computation can be done as:. The authors in [19] proposed a modeling of an They consider nodes in a network, with the presence of greedy nodes modifying the backoff timer. The misbehaving nodes choose a random backoff interval in the range of , where and W is. The collision probability at the greedy node is.

    Therefore they modified the stochastic process proposed in [18] to establish a simple modeling for the misbehaving nodes. However, finding a closed form for each variable is not our goal, since our approach is based on simulation analysis. The basic idea of our strategy for detecting IEEE We showed that this misbehavior led to an increase of the average reception throughput and a decrease of times between receptions for the greedy nodes. On the other side it generates a reverse effect for honest nodes [3].

    Our detection method is based on the supervision of the two metrics defined in our previous work [3] and its dispersion by a control chart with two limits.

    Detection and Prevention of MAC Layer Misbehavior for Ad Hoc Networks

    These graphs are called control charts, following a statistical process control approach. One of the basic principles of this control is deviation detection. All variations on a system do not require modification. Indeed, two processes are never exactly. There are many sources of variation in low amplitude that cannot be removed, all of them representing the common causes of dispersion [13].

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    • MAC layer misbehavior in ad hoc networks - IEEE Conference Publication;
    • On the resiliency of mobile ad hoc networks to MAC layer misbehavior.

    However, there are major causes of variation that require change.