News & Views | Published:

Computing

The wireless epidemic

Nature volume 449, pages 287288 (20 September 2007) | Download Citation

As wireless communication technologies spread, so the potential for viruses to exploit them grows. Biological models of virus transmission will assume new relevance for assessing the emerging threat.

Human mobility and wireless networking could combine to abet the spread of computer viruses. Image: WWW.JUPITERIMAGES.COM

Ever since the first appearance of computer viruses on the digital landscape, our understanding of them has drawn on parallels with biology. Analogues of mutation, phylogenetic reconstruction and computational 'immune systems'1 have all been investigated. But the central analogy has come from the use of epidemiological models to track how a computer virus spreads. A crucial ingredient in these models is a description of the contact network through which the epidemic propagates, where the links represent who has the potential to infect whom. Traditionally, computer viruses have propagated on networks that bear little resemblance to the networks of physical contact through which their biological counterparts spread. But a growing body of research2,3,4,5,6,7,8 shows that the increasing use of short-range wireless communication networks might cause the two models to converge.

Accurately modelling the network through which a disease epidemic spreads is difficult in almost any setting. Diseases in plant populations, or animal diseases such as rabies, are heavily constrained by geographical proximity and the relatively fixed physical locations of the infected individuals9. Models of these diseases have been extended using detailed data on patterns of travel within cities10,11 and by air worldwide12 in attempts to analyse disease outbreaks in human populations.

Epidemics on the Internet are even more diverse. At the most general level, there is a distinction between computer viruses, which 'piggyback' on data exchanged between users, and computer worms, which more actively direct their own transmission through a network13. The networks on which these types of malicious code spread are based on patterns of file transfer or e-mail communication, or even on structures that evolve implicitly as a computer worm scans the Internet for targets.

Mathematical models of these different networks lie at various points on a broad conceptual spectrum. At one end are network models that reflect strong spatial effects, with nodes at fixed positions in two dimensions, each connected to a small number of other nodes a short distance away9. At the other end are 'scale-free' networks, which are essentially unconstrained by physical proximity, and in which the number of contacts per node are widely spread14. Models based on human travel data occupy an intermediate position in this spectrum of spatial constraints. The different network structures lead in turn to qualitative differences in the way epidemics spread: whereas epidemics can persist at arbitrarily low levels of virulence in scale-free networks14,15, epidemics in simple two-dimensional models need a minimum level of virulence to prevent them from dying out quickly9.

Very roughly, then, one could view models of biological epidemics as rooted in spatial networks, and expanding into less spatial realms to model the technologies that have accelerated human travel. Meanwhile, research on cyber-epidemics has occupied the non-spatial end of the spectrum, with its diverse and far-flung connections, when modelling global communication technologies such as the Internet.

But the spread of short-range wireless communication technologies such as Bluetooth, and the emergence of worms that exploit these systems2,3,4, is disrupting this dichotomy by making possible computer-virus outbreaks whose progress closely tracks human mobility patterns. These types of wireless worm are designed to infect mobile devices such as cell phones, and then to continuously scan for other devices within a few tens of metres or less, looking for new targets. A computer virus thus becomes something you catch not necessarily from a compromised computer halfway around the world, but possibly from the person sitting next to you on a bus, or at a nearby table in a restaurant.

Wireless worms can also be used to attack 'mobile ad-hoc networks' (MANETs), which are designed to connect devices such as cheap, low-powered sensors using short-range wireless communication5. These networks have applications in environmental monitoring, disaster relief and military operations; when the nodes of such a system are placed in relatively fixed positions, there is a close analogy to some of the oldest and best-studied models of disease epidemics, based on short-range spatial contacts in two dimensions9.

Although these types of worm have not yet achieved widespread penetration, prototypes have successfully exploited vulnerabilities in wireless protocols including Bluetooth, and it is to be expected that mobile devices will be increasingly targeted by malicious code. In assessing the risks of such attacks, and developing countermeasures against them, it is intriguing to contemplate how we might draw on expertise from the field of human epidemiology in understanding how contagion spreads.

These analogies will, of course, always be incomplete. In particular, the timescales over which highly successful mobile worms operate will probably be shorter than those of their biological counterparts. The initial outbreak, and the opportunities for recovery, will potentially progress much more quickly. Mobile worms are also restricted in a way that has no obvious biological analogy by the limited communication rates of the devices they infect. Particularly aggressive worms will also be confronted with the 'self-throttling' effects of many infected devices competing for limited wireless bandwidth5,6.

Analogies to biological epidemics can also be exploited for beneficial purposes, in the design of computer-network protocols16. For mobile devices, epidemiology helps in dealing with the problem of intermittent connectivity: that the routing of traffic must conform to a dynamic and unpredictable network structure as the owners of mobile devices move around. The result is a growing interest in opportunistic routing, in which messages are passed between devices that come into physical proximity, with the goal of eventually reaching a specified recipient7. The development of such protocols has drawn on detailed data concerning human mobility and contact patterns8.

These lines of work reinforce how the evolution of computer viruses and worms has always been closely linked with the legitimate concerns of computer networking. As that relationship extends into the domain of mobile devices, we are taking further steps towards a world where digital traffic flows not just over the wired backbone of the Internet, but also in small leaps through physical space as people pass one another on the street.

References

  1. 1.

    , , & Sci. Am. 88–93 (Nov. 1997).

  2. 2.

    & Proc. ACM Workshop Wireless Security 77–86 (ACM, New York, 2005).

  3. 3.

    et al. Proc. 4th ACM Workshop Rapid Malcode 9–16 (ACM, New York, 2006).

  4. 4.

    , , & Proc. 5th ACM Int. Conf. Mob. Syst. Appl. Serv. (ACM, New York, 2007).

  5. 5.

    New J. Phys. 9, 189 (2007).

  6. 6.

    , , & Proc. 19th IEEE Workshop Parallel Distr. Simul. 207–214 (IEEE, New York, 2005).

  7. 7.

    et al. Proc. ACM SIGCOMM Workshop Delay-Tolerant Networks 244–251 (ACM, New York, 2005).

  8. 8.

    et al. IEEE Trans. Mobile Comput. 6, 606–620 (IEEE, New York, 2007).

  9. 9.

    SIAM Rev. 41, 677–718 (1999).

  10. 10.

    et al. Nature 429, 180–184 (2004).

  11. 11.

    , , , & J. Theor. Biol. 232, 71–81 (2005).

  12. 12.

    , , & Proc. Natl Acad. Sci. USA 103, 2015–2020 (2006).

  13. 13.

    , , & Proc. 1st ACM Workshop Rapid Malcode 11–18 (ACM, New York, 2003).

  14. 14.

    & Phys. Rev. Lett. 86, 3200–3203 (2000).

  15. 15.

    , , & Proc. 16th ACM Symp. Discr. Algor. 301–310 (ACM, New York, 2005).

  16. 16.

    et al. Proc. 6th ACM Symp. Princip. Distr. Comput. 1–12 (ACM, New York, 1987).

Download references

Author information

Affiliations

  1. Jon Kleinberg is in the Department of Computer Science, Cornell University, Ithaca, New York 14853, USA. kleinber@cs.cornell.edu

    • Jon Kleinberg

Authors

  1. Search for Jon Kleinberg in:

About this article

Publication history

Published

DOI

https://doi.org/10.1038/449287a

Further reading

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Newsletter Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing