Online criminals have evolved their tactics to harden their botnets against takedown using a variety of tactics, including fast-flux networks and Conficker-like dynamic domain generation. Yet, such tactics can also pinpoint when such networks are being created by bot operators, according to research from the Georgia Institute of Technology.
The research found that dynamically detecting changes in the domain name system (DNS) can lead to the early detection of botnets. When bot masters create the infrastructure for a botnet, the reputation of the domain names can tip off defenders.
In two papers, one released last year (PDF) and one to be published in September, GATech researchers found that they can detect anomalies in the domain name system indicative of botnets and have documented recognition rates greater than 98 percent.
On Monday, network security firm Damballa announced a service based on the research to provide intelligence on botnet-infected systems. Called FirstAlert, the service can detect the characteristic DNS queries indicative of botnet infections inside a customer's network.
"If you can detect the domain abuse early enough in the infection lifecycle, then you can get ahead of the threat," says David Holmes, vice president of marketing for Damballa. "If we see a domain lookup in a customer environment we haven't seen before, we can say, that's interesting."
The two papers describe two systems. One, Notos, dynamically determines the reputation of a domain-name/IP-address pairs. The system collects DNS query data from registrars and analyses the domain structure, focusing on the network and zone characteristics.
"It builds models of known legitimate domains and malicious domains, and uses these models to compute a reputation score for a new domain indicative of whether the domain is malicious or legitimate," writes Manos Antonakakis, a researcher at GATech and co-author of the paper.
The other, Kopis, can detect changes across the DNS infrastructure of a company, Internet service provider or the global Internet, that is characteristic of malicious networks. The systems require about 5 days of training to begin to detect botnets, Holmes says.
"Kopis is a machine learning technology," he says. "It has been trained or can be trained to understand lookup patterns and periodicity and profiles ... based on the diversity of the lookups."
The systems used together have been able to detect botnets, such as the IMDDOS and those built on SpyEye. Many times, it can detect botnets weeks before they actually go active and start sending out malware, Holmes says.
The technology is not meant to be used as a standalone service, but in conjunction with other expert systems such as spam engines. Notos, for example, will penalise legitimate websites that are hosted with a provider that also hosts malicious domain names.
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