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GCN : September 2013
GCN SEPTEMBER 2013 • GCN.COM 7 The Energy Department's National Renewable Energy Laboratory (NREL) has developed Energy DataBus, an open-source application for monitor- ing, storing and analyzing energy-relat- ed data for optimizing energy use and detecting energy "leaks." The free software is available for download from GitHub and can be used by organizations to do their own energy data research. What makes the application special is its use of a parallel-processing database that allows it to manage the massive amounts of data generated by energy sensors running 24/7 in buildings. "We're using a new type of data store called NoSQL," said Dean Hiller, archi- tect on the NREL team that developed the Energy DataBus. Speci cally, the Energy DataBus employs Apache's Cassandra database. "That allows us to collect massive amounts of data," Hiller said. "We just keep adding data nodes. Not only that, we can have a higher throughput. See, we can be doing 100,000 events per second or a million events per second. All that is required is that we add more computers." Another feature that distinguishes the Energy DataBus is that, like Cas- sandra, it was designed using open- source software. "There are a lot of advantages with open source," said Keith Searight, proj- ect leader for the Energy DataBus. "It's free, it's easy and it has a low barrier to entry. And then you can leverage all the value that you get from other people in your community. In that way you can get a better product for a low price. You can build a community rapidly when there is a low hurdle." The team also uses PlayORM, an open-source program for integrating data from different sources. That's important, since the Energy DataBus is receiving data from a wide variety of sources, including power meters, thermometers, carbon-dioxide sensors, air-conditioning equipment and me- teorological sensors. "All of those are pulled from around the NREL campus," Searight said. The variety of data offered another challenge to the Energy DataBus de- velopers. "One of the problems with all these sensors is that you have a huge amount of data, and they all work with different times," Searight said. DataBus can align the data points, giving them a common time stamp so analysts can draw graphs or see averages or totals. • NREL releases free, open- source energy analysis tool BY PATRICK MARSHALL Network administrators and security of cials could soon have a new tool to help detect malicious traf c on their networks by sifting out the command and control traf c of infected comput- ers from the background noise. Researchers from the Georgia Insti- tute of Technology tested a prototype of the tool, called ExecScent, on live networks and identi ed dozens of pre- viously unknown command and control domains while discovering hundreds of infected hosts on the networks. ExecScent spots the traf c by using templates of common command and control protocols used by malware. What sets it apart is that it also uses machine learning to understand the normal traf c patterns. "It learns to adapt to normal back- ground traf c," said Mustaque Aha- mad, professor at Georgia Tech's College of Computing. By spotting traf c that is both similar to known examples of C&C communication but different from normal traf c, it reduces the number of false positives. Modern malware on an infected host typically communicates with a com- mand and control server to send home stolen data and receive instructions. Tracing this traf c can be a way of spotting infections and identifying their source, but because attackers often use multiple servers on rapidly shifting domains, identifying the traf c is not always easy. ExecScent takes advantage of the fact that command and control protocols often are reused in multiple variants of malware, which can make them easier to spot. Looking for known patterns and signatures is not new, but distinguishing them in real time in high volumes of network traf c can be a challenge. The ability to learn and adapt to network norms helps expose the malicious traf c. • Cyber tool learns network behavior to sniff out malware BY WILLIAM JACKSON