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Researchers Develop Strategy To Pinpoint Malicious Activity

Researchers from the Oak Ridge National Laboratory and Indiana University have developed a technique they say could help identify where the bad elements are hanging out.

"Malicious activity is not necessarily evenly distributed across the Internet," states a paper describing the joint-research group's initial work. "This analysis shows that there are dense clusters of malicious activity in the Internet."

The researchers performed statistical analysis of IP addresses contained in blacklists commonly used for filtering and blocking malicious activity to see if they could identify Internet service providers, hosting services or other autonomous systems as having a disproportionate share of harmful electronic activity. This could also help ISPs and other organizations to evaluate their own conditions, impacting the way they make decisions about prioritizing traffic.

"We wanted to be able to say if a particular network is dong a good job of cleaning up its machines," said Craig Shue, cybersecurity research scientist at the Oak Ridge National Laboratory's Computational Sciences and Engineering Division.

They found that not only were some doing a poor job of cyber hygiene, but that a few appeared to be overtly malicious. "We found four spectacularly bad ISPs that were big blips on the radar," Shue said.

Shue, along with Andrew Kalafut and Minaxi Gupta of Indiana University's School of Informatics and Computing, will be presenting the results of their research at the IEEE Infocom conference in San Diego next week.

In a few cases, autonomous systems responsible for malicious activity have been cut off or shut down, such as Atrivo, McColo and Pricewert Networks. But generally, "ISPs have never had any motivation to clean up their acts," Shue said.

He and his collaborators used data from 12 common blacklist services on millions of IP addresses associated with spam, phishing, malware and botnet activities. When possible, host names were resolved to IP addresses and the addresses associated with particular systems. The researchers then evaluated the data to determine the percentage of a system's addresses that were blacklisted and the percentage of a blacklist that a system hosted.

"Very few had more than 0.5 percent bad addresses," Shue said. "The ones that have more than that jump to the top." Some autonomous systems have more than 80 percent of their routable IP address space blacklisted, and others account for large fractions between 50 and 80 percent of their addresses blacklisted.

Three U.S.-based hosting providers accounted for more than 6 percent of at least one of the blacklists -- a disproportionately large percentage for the size of the systems.

"This indicates that some [autonomous systems] have either too lax of a security policy or may be intentionally harboring cyber crime," the researchers conclude in their paper.

Despite the results, traffic cannot simply be declared malicious solely because it originated from one of the systems with a high degree of maliciousness, and it is too early to identify the bad "actors, Shue said.

"We have a little difficulty with naming names," he said, because of liability and the preliminary nature of the work. Shue discussed that the quality of the blacklist data the work was based on is a concern because there are few industry standards for compiling and maintaining the lists. There are often no provisions for removing addresses from a blacklist once they appear, so the largest lists might contain data that is no longer accurate.

The next step for Shue and his researchers is to evaluate the quality of blacklist data.

About the Author

William Jackson is the senior writer for Government Computer News (GCN.com).

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