IDEAS home Printed from
   My bibliography  Save this article

A virus spreading model for cognitive radio networks


  • Hou, L.
  • Yeung, K.H.
  • Wong, K.Y.


Since cognitive radio (CR) networks could solve the spectrum scarcity problem, they have drawn much research in recent years. Artificial intelligence(AI) is introduced into CRs to learn from and adapt to their environment. Nonetheless, AI brings in a new kind of attacks specific to CR networks. The most powerful one is a self-propagating AI virus. And no spreading properties specific to this virus have been reported in the literature. To fill this research gap, we propose a virus spreading model of an AI virus by considering the characteristics of CR networks and the behavior of CR users. Several important observations are made from the simulation results based on the model. Firstly, the time taken to infect the whole network increases exponentially with the network size. Based on this result, CR network designers could calculate the optimal network size to slow down AI virus propagation rate. Secondly, the anti-virus performance of static networks to an AI virus is better than dynamic networks. Thirdly, if the CR devices with the highest degree are initially infected, the AI virus propagation rate will be increased substantially. Finally, it is also found that in the area with abundant spectrum resource, the AI virus propagation speed increases notably but the variability of the spectrum does not affect the propagation speed much.

Suggested Citation

  • Hou, L. & Yeung, K.H. & Wong, K.Y., 2012. "A virus spreading model for cognitive radio networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(24), pages 6632-6644.
  • Handle: RePEc:eee:phsmap:v:391:y:2012:i:24:p:6632-6644 DOI: 10.1016/j.physa.2012.07.048

    Download full text from publisher

    File URL:
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    1. Szolnoki, Attila & Perc, Matjaž & Danku, Zsuzsa, 2008. "Towards effective payoffs in the prisoner’s dilemma game on scale-free networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(8), pages 2075-2082.
    2. Wu, Zhi-Xi & Guan, Jian-Yue & Xu, Xin-Jian & Wang, Ying-Hai, 2007. "Evolutionary prisoner's dilemma game on Barabási–Albert scale-free networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 379(2), pages 672-680.
    3. Qin, Shao-Meng & Zhang, Guo-Yong & Chen, Yong, 2009. "Coevolution of game and network structure with adjustable linking," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(23), pages 4893-4900.
    4. A. Szolnoki & M. Perc, 2009. "Promoting cooperation in social dilemmas via simple coevolutionary rules," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 67(3), pages 337-344, February.
    5. Fu, Feng & Chen, Xiaojie & Liu, Lianghuan & Wang, Long, 2007. "Promotion of cooperation induced by the interplay between structure and game dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 383(2), pages 651-659.
    Full references (including those not matched with items on IDEAS)


    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.

    Cited by:

    1. Tomovski, Igor & Kocarev, Ljupčo, 2015. "Network topology inference from infection statistics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 272-285.


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:391:y:2012:i:24:p:6632-6644. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu). General contact details of provider: .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.