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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
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WorldCat: 643243560
doi: 10.4316/AECE


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  2/2012 - 1
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Performance Analysis of Cell-Phone Worm Spreading in Cellular Networks through Opportunistic Communications

YAHUI, W. See more information about YAHUI, W. on SCOPUS See more information about YAHUI, W. on IEEExplore See more information about YAHUI, W. on Web of Science, DENG, S. See more information about  DENG, S. on SCOPUS See more information about  DENG, S. on SCOPUS See more information about DENG, S. on Web of Science, HUANG, H. See more information about  HUANG, H. on SCOPUS See more information about  HUANG, H. on SCOPUS See more information about HUANG, H. on Web of Science, DENG, Y. See more information about DENG, Y. on SCOPUS See more information about DENG, Y. on SCOPUS See more information about DENG, Y. on Web of Science
 
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Download PDF pdficon (720 KB) | Citation | Downloads: 1,425 | Views: 4,747

Author keywords
opportunistic communications, delay tolerant network, 3G networks, Markov process, cell-phone worms

References keywords
mobile(14), networks(10), malware(10), sarkar(5), khouzani(5), infocom(5), infcom(5), worm(4), network(4), delay(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2012-05-30
Volume 12, Issue 2, Year 2012, On page(s): 3 - 8
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2012.02001
Web of Science Accession Number: 000305608000001
SCOPUS ID: 84865306374

Abstract
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Worms spreading directly between cell-phones over short-range radio (Bluetooth, WiFi, etc.) are increasing rapidly. Communication by these technologies is opportunistic and has very close relation with the social characteristics of the phone carriers. In this paper, we try to evaluate the impact of different characteristics on the spreading performance of worms. On the other hand, the behaviors of worms may have certain impact, too. For example, worms may make phones be completely dysfunctional and these phones can be seen as killed. We study the impact of the killing speed. Using the Markov model, we propose some theoretical models to evaluate the spreading performance in different cases. Simulation results show the accuracy of our models. Numerical results show that if users do not believe the data coming from others easily, the worms may bring less damage. Surprisingly, if the users are more willing to install the anti-virus software, the worms may bring bigger damage when the software becomes to be outdated with high probability. Though the worms can bring big damage on the network temporarily by killing phones rapidly, numerical results show that this behavior may decrease the total damage in the long time. Therefore, killing nodes more rapidly may be not optimal for worms.


References | Cited By  «-- Click to see who has cited this paper

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[CrossRef] [Web of Science Times Cited 42]


[2] F. Li, Y. Yang, and J. Wu, "CPMC: an efficient proximity malware coping scheme in Smartphone-based mobile networks," in Proc. IEEE INFOCOM, 2010.
[CrossRef]


[3] K. Fall, "A delay-tolerant network architecture for challenged internets," in Proc. ACM SIGCOMM, 2003.
[CrossRef]


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[CrossRef] [Web of Science Times Cited 48]


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[CrossRef]


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[CrossRef] [Full Text] [Web of Science Times Cited 16]


[7] T. Spyropoulos, T. Turletti, and K. Obrazcka, "Routing in delay tolerant networks comprising heterogeneous populations of nodes," IEEE Trans. Mobile Computing, vol.8, no. 8, Aug. 2009.
[CrossRef] [Web of Science Times Cited 122]


[8] E. Bulut, Z. Wang, and B. Szymanski, "Cost effective multi-period spraying for routing in delay tolerant networks," IEEE/ACM Trans. Networking, Vol. 8, no. 5, Oct. 2010.
[CrossRef] [Web of Science Times Cited 61]


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[CrossRef] [Web of Science Times Cited 54]


[10] MHR. Khouzani and S. Sarkar, "Dynamic malware attack in energy-constrained mobile wireless networks." in Proc. Fifth Symposium on Information Theory and Applications, 2010.
[CrossRef]


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[CrossRef]


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[CrossRef]


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[CrossRef] [Web of Science Times Cited 75]


[14] N. Husted and S. Myers, "Why mobile-to-mobile wireless malware won't cause a storm," in Proc. USENIX Workshop on Large-scale Exploits and Emergent Threats, 2011.

[15] J. Tang, C. Mascolo, M. Musolesi, and V. Latora, "Exploiting temporal complex network metrics in mobile malware containment," in Proc. WOWMOM, Jue. 2011.
[CrossRef]


[16] G. Yan, L. Cuellar, and S. Eidenbenz, "Blue-watchdog: detecting Bluetooth worm propagation in public areas," in Proc. DSN, 2009.
[CrossRef] [Web of Science Times Cited 6]


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[CrossRef]


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[CrossRef]


[19] MHR. Khouzani, E. Altman, and S. Sarkar, "Maximum damage malware attack in mobile wireless networks," in Proc. IEEE INFOCOM, 2010.
[CrossRef]


[20] MHR. Khouzani, S. Sarkar, and E. Altman, "Dispatch then stop: optimal dissemination of security patches in mobile wireless networks," in Proc. IEEE CDC, 2010.
[CrossRef] [Web of Science Times Cited 18]


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[CrossRef]


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[CrossRef]


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[CrossRef]




References Weight

Web of Science® Citations for all references: 772 TCR
SCOPUS® Citations for all references: 0

Web of Science® Average Citations per reference: 29 ACR
SCOPUS® Average Citations per reference: 0

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2024-03-26 15:46 in 143 seconds.




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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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