About: The United States’s energy grid could fall into victim to numerous cyber attacks resulting in unprecedented damage to national security. The smart concept devices including electric automobiles, smart homes and cities, and the Internet of Things (IoT) promise further integration but as the hardware, software, and network infrastructure becomes more integrated they also become more susceptible to cyber attacks or exploitation. The Defense Information Systems Agency (DISA)’s Big Data Platform (BDP), deep analytics, and unsupervised machine learning (ML) have the potential to address resource management, cybersecurity, and energy network situation awareness. In this paper, we demonstrate their potential using the Pecan Street data. We also show an unsupervised ML such as lexical link analysis (LLA) as a causal learning tool to discover the causes for anomalous behavior related to energy use and cybersecurity.   Goto Sponge  NotDistinct  Permalink

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  • The United States’s energy grid could fall into victim to numerous cyber attacks resulting in unprecedented damage to national security. The smart concept devices including electric automobiles, smart homes and cities, and the Internet of Things (IoT) promise further integration but as the hardware, software, and network infrastructure becomes more integrated they also become more susceptible to cyber attacks or exploitation. The Defense Information Systems Agency (DISA)’s Big Data Platform (BDP), deep analytics, and unsupervised machine learning (ML) have the potential to address resource management, cybersecurity, and energy network situation awareness. In this paper, we demonstrate their potential using the Pecan Street data. We also show an unsupervised ML such as lexical link analysis (LLA) as a causal learning tool to discover the causes for anomalous behavior related to energy use and cybersecurity.
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  • United States
  • Distributed computing problems
  • Technology forecasting
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