Skip to Content
Uncategorized

The ‘Nature’ of Net Viruses

Canadian ecologists are studying the spread of Internet viruses to better understand the invasion of non-native insects – but can the actions of Web-based interlopers really offer answers on nature’s original hackers?

Scientists could learn a lot about the way natural systems work from the decidedly unnatural world of the Internet, according to research published earlier this year.

Two Canadian ecologists at the University of Windsor in Ontario have been studying the way that Internet viruses proliferate to better determine the progress of a real-world intruder – the spiny water flea, an insect that’s native to Russia that has been invading the Canadian lake system for two decades. 

Their approach might seem, well, a little buggy. But Professor Hugh MacIsaac and graduate student Jim Muirhead published a paper in March on their work in the British Ecological Society’s Journal of Applied Ecology which says that by applying the rules of network theory and taking insights from how information spreads across the Internet, they’ve constructed a picture of the way their ecological interloper operates.

Surprisingly, that picture, they say, is not too dissimilar from the movements of Bagle or MyDoom, two of the most pervasive Internet viruses to hit the Web.

MacIsaac, the invasive species research chairman for the university’s department of fisheries and oceans, says that rather than seeing a random dispersal of these non-native insects –which have spread to more than 57 of Canada’s inland lakes since the 1980s, their expansion in many ways mirrors that of the Internet virus.

For example, Internet viruses tend to spread fastest when they attack the most broadly used email programs and servers, moving downstream from these major hubs of activity.  Internet security companies can often nip viruses in the bud by tracking down their source and mapping how they spread,

Along the same lines, MacIsaac and Muirhead discovered that certain lakes are more likely to develop as ”invasion hubs” for their flea, since these lake-hubs attract more people boating and fishing.

By tracking boaters and determining their movement, MacIsaac says they “can determine which lake was the source of invasion.” 

Some might question how a technological manifestation like a computer-based virus could provide insight into the workings of an ecosystem.  But MacIsaac is quick to point out that the invasion of his water flea  –  much like the intrusion of an online virus  –  was crafted by forces outside of nature.  The spiny water flea didn’t move from Russia to Canada on its own power, but is widely thought to have traveled in shipping containers that moved from one country to the other. 

Similarly, the spread of the bugs has largely been facilitated by human intervention – fishermen transporting spiny water flea eggs on their gear and their boats from one big popular lake, to another that’s more remote.

The goal of the research is to help predict where these bugs will spread next, and therefore allow ecologists to aim their limited resources in places that help prevent further invasion. This line of research, says MacIsaac, was born because he was “very unsatisfied with the models to predict the spread of invasive species,” which typically focus too heavily on the attributes of the species itself and not its movements, which are often influenced by outside forces.

Mark Buchanan, author of Nexus: Small Worlds and the Groundbreaking Science of Networks, sees tremendous potential for investigating the parallels between such seemingly different networks.

“Whether you are talking about Internet viruses or real-world viruses, you should be aware of the network structure that influences where and when spreading is likely to take place,” Buchanan says.  “In either case, you want to target your efforts at those hubs where most of the dispersal is centered.”

Studies have shown that even with human viruses, this is the most effective way to detect the outbreak of a new virus such as Avian flu. Once hubs have been identified, researchers must  monitor heavily-trafficked locations from which it may be emanating, according to Buchanan.

But these arguments don’t necessarily hold water for some Internet virus experts, who say the analogies between technology and ecology in this case are strained at best.

“(The research) is a colossal waste of time and money,” says Vincent Gullotto, vice president for McAfee’s Anti-Virus Emergency Response Team (AVERT).  He says such studies, which seek to draw conclusions from the Internet about how human viruses or ecological interlopers might spread, “are pretty thin” since the environments are so different.

For example, in the online world, viruses are written with a specific intent and attack a static number of machines, he says, whereas the natural environment is ever-changing. That fundamental difference  –  programming versus reaction  –  makes it impossible to conduct any true comparisons, says Richard Wang, manager of the U.S. virus lad for Sophos, a British-based firm that provides anti-virus software and services primarily to businesses.

“It’s a little unfortunate that people have preconceived ideas about what it means to ‘spread’ and ‘mutate’,” says Wang. “If you take the analogy too far, it breaks down.” 

Ecological changes, even if influenced by man, Wang points out, are more “spontaneous” and thus not the same as the “deliberate act” of creating an Internet virus.  While similar terms used in both ecology and the Internet may imply deeper similarities between the two worlds, Wang says “the systems aren’t really parallel.”

Nonetheless, other experts do see meaningful results that could be derived from such studies. 

Ero Carrera, an anti-virus researcher with F-Secure Corp., believes that while there are major differences between real-world networks and their virtual counterparts, the basic rules of complexity theory can apply to everything from computer networks to crowds of people to the invasion of foreign insects.

“I think Internet viruses, being so far much simpler than any biological counterpart, provide an easy testbed and a comprehensible model to analyze and develop mathematical abstractions from,” Carrera says. “In all these cases, natural and virtual, when seen from afar, the same rules apply.”

Keep Reading

Most Popular

Large language models can do jaw-dropping things. But nobody knows exactly why.

And that's a problem. Figuring it out is one of the biggest scientific puzzles of our time and a crucial step towards controlling more powerful future models.

The problem with plug-in hybrids? Their drivers.

Plug-in hybrids are often sold as a transition to EVs, but new data from Europe shows we’re still underestimating the emissions they produce.

Google DeepMind’s new generative model makes Super Mario–like games from scratch

Genie learns how to control games by watching hours and hours of video. It could help train next-gen robots too.

How scientists traced a mysterious covid case back to six toilets

When wastewater surveillance turns into a hunt for a single infected individual, the ethics get tricky.

Stay connected

Illustration by Rose Wong

Get the latest updates from
MIT Technology Review

Discover special offers, top stories, upcoming events, and more.

Thank you for submitting your email!

Explore more newsletters

It looks like something went wrong.

We’re having trouble saving your preferences. Try refreshing this page and updating them one more time. If you continue to get this message, reach out to us at customer-service@technologyreview.com with a list of newsletters you’d like to receive.