Recent cyber security events have demonstrated the need for algorithms that adapt to the rapidly evolving threat landscape of complex network systems. In particular, human analysts often fail to identify data exfiltration when it is encrypted or disguised as innocuous data. Signature-based approaches for identifying data types are easily fooled and analysts can only investigate a small fraction of network events. However, neural networks can learn to identify subtle patterns in a suitably chosen input space. To this end, we have developed a signal processing approach for classifying data files which readily adapts to new data formats. We evaluate the performance for three input spaces consisting of the power spectral density, byte probability distribution and sliding-window entropy of the byte sequence in a file. By combining all three, we trained a deep neural network to discriminate amongst nine common data types found on the Internet with 97.4% accuracy.
This report provides a detailed overview of the work performed for project number 130781, 'A Systems Biology Approach to Understanding Viral Hemorrhagic Fever Pathogenesis.' We report progress in five key areas: single cell isolation devices and control systems, fluorescent cytokine and transcription factor reporters, on-chip viral infection assays, molecular virology analysis of Arenavirus nucleoprotein structure-function, and development of computational tools to predict virus-host protein interactions. Although a great deal of work remains from that begun here, we have developed several novel single cell analysis tools and knowledge of Arenavirus biology that will facilitate and inform future publications and funding proposals.