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A Signal Processing Approach for Cyber Data Classification with Deep Neural Networks

Procedia Computer Science

Cox, Jonathan A.; James, Conrad D.; Aimone, James B.

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.

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TYPE Conference Poster YEAR 2015
ScopusOSTI

Using High Performance Computing to Examine the Processes of Neurogenesis Underlying Pattern Separation and Completion of Episodic Information

Aimone, James B.; Bernard, Michael L.; Vineyard, Craig M.; Verzi, Stephen J.

Adult neurogenesis in the hippocampus region of the brain is a neurobiological process that is believed to contribute to the brain's advanced abilities in complex pattern recognition and cognition. Here, we describe how realistic scale simulations of the neurogenesis process can offer both a unique perspective on the biological relevance of this process and confer computational insights that are suggestive of novel machine learning techniques. First, supercomputer based scaling studies of the neurogenesis process demonstrate how a small fraction of adult-born neurons have a uniquely larger impact in biologically realistic scaled networks. Second, we describe a novel technical approach by which the information content of ensembles of neurons can be estimated. Finally, we illustrate several examples of broader algorithmic impact of neurogenesis, including both extending existing machine learning approaches and novel approaches for intelligent sensing.

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TYPE SAND Report YEAR 2014
OSTIDOI

Regulation and Function of Adult Neurogenesis. From Genes to Cognition

Physiological Reviews

Aimone, James B.

Adult neurogenesis in the hippocampus is a notable process due not only to its uniqueness and potential impact on cognition but also to its localized vertical integration of different scales of neuroscience, ranging from molecular and cellular biology to behavior. Our review summarizes the recent research regarding the process of adult neurogenesis from these different perspectives, with particular emphasis on the differentiation and development of new neurons, the regulation of the process by extrinsic and intrinsic factors, and their ultimate function in the hippocampus circuit. Arising from a local neural stem cell population, new neurons progress through several stages of maturation, ultimately integrating into the adult dentate gyrus network. Furthermore, the increased appreciation of the full neurogenesis process, from genes and cells to behavior and cognition, makes neurogenesis both a unique case study for how scales in neuroscience can link together and suggests neurogenesis as a potential target for therapeutic intervention for a number of disorders.

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TYPE Journal Article YEAR 2014
OSTIDOI

Development characterization and modeling of a TaOx ReRAM for a neuromorphic accelerator

Marinella, Matthew J.; Mickel, Patrick R.; Lohn, Andrew L.; Hughart, David R.; Bondi, Robert J.; Mamaluy, Denis M.; Hjalmarson, Harold P.; Stevens, James E.; Decker, Seth D.; Apodaca, Roger A.; Evans, Brian R.; Aimone, James B.; Rothganger, Fredrick R.; James, Conrad D.; DeBenedictis, Erik

This report discusses aspects of neuromorphic computing and how it is used to model microsystems.

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TYPE Conference Poster YEAR 2014
OSTIDOI

Neurons to algorithms LDRD final report

Aimone, James B.; Warrender, Christina E.; Trumbo, Derek T.

Over the last three years the Neurons to Algorithms (N2A) LDRD project teams has built infrastructure to discover computational structures in the brain. This consists of a modeling language, a tool that enables model development and simulation in that language, and initial connections with the Neuroinformatics community, a group working toward similar goals. The approach of N2A is to express large complex systems like the brain as populations of a discrete part types that have specific structural relationships with each other, along with internal and structural dynamics. Such an evolving mathematical system may be able to capture the essence of neural processing, and ultimately of thought itself. This final report is a cover for the actual products of the project: the N2A Language Specification, the N2A Application, and a journal paper summarizing our methods.

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TYPE SAND Report YEAR 2013
OSTIDOI
Results 151–175 of 184
Results 151–175 of 184