Large Scale Enrichment and Statistical Cyber Characterization of Network Traffic

Modern network sensors continuously produce enormous quantities of raw data that are beyond the capacity of human analysts. Cross-correlation of network sensors increases this challenge by enriching every network event with additional metadata. These large volumes of enriched network data present opportunities to statistically characterize network traffic and quickly answer a key question: "What are the primary cyber characteristics of my network data?" The Python GraphBLAS and PyD4M analysis frameworks enable anonymized statistical analysis to be performed quickly and efficiently on very large network data sets. This approach is tested using billions of anonymized network data samples from the largest Internet observatory (CAIDA Telescope) and tens of millions of anonymized records from the largest commercially available background enrichment capability (GreyNoise). The analysis confirms that most of the enriched variables follow expected heavy-tail distributions and that a large fraction of the network traffic is due to a small number of cyber activities. This information can simplify the cyber analysts' task by enabling prioritization of cyber activities based on statistical prevalence.Comment: 8 pages, 8 figures, HPE

PRACTITIONERS VIEW ON QUICK STUDY OF SNOWPACK: HOW TO EXPLAIN THE VOCABULARY FOR POLE PROBE TESTS AND SLOPE CUTTING

ABSTRACT: As practitioners and heli-ski guides in the Chugach Mountains of Valdez, Alaska we ski cut slopes to mitigate the volume of loose snow avalanches (sluff). We determine if results of slope cuts are probable or not through pole probing the structure and hardness of the snowpack. This includes the top 120cms of the snowpack as this is a common length of a ski pole. We are introducing a new vernacular that clearly describes pole probing and its correlation to slope cut results. Our adage includes data codes used to quickly and easily decipher the structure of the snowpack as well as describe varying degrees of loose snow avalanches. Depths of different hardness are also quantifiable. In a right side up snowpack hardness increases as depth increases. A right side up pole probe with an impenetrable hard layer 45cms down is expressed as PPRU45I. The value after the shorthand represents the depth at which the pole probe becomes impenetrable. Furthermore, slopes with upside down pole probes, where changes in hardness become inconsistent, necessitate a snow pit. We include both CT and ECT as the snowpack may demonstrate a failure in compression but not in shear. We have also elaborated on the existing slope cut data codes used in table 2.12 on page 55 of the 'Snow, Weather and Avalanches: Observation Guidelines for Avalanche Programs in the United States' (2009) to include quantifiable amounts of loose snow avalanches. Daily we experience a variety of snow conditions on different aspects and elevations. Creating a dialogue based upon pole probing and slope cutting has improved our efficiency and communication regarding snowpack structure, sluff management and spatial variability

Laminate polyethylene window development for large aperture millimeter receivers

New experiments that target the B-mode polarization signals in the Cosmic Microwave Background require more sensitivity, more detectors, and thus larger-aperture millimeter-wavelength telescopes, than previous experiments. These larger apertures require ever larger vacuum windows to house cryogenic optics. Scaling up conventional vacuum windows, such as those made of High Density Polyethylene (HDPE), require a corresponding increase in the thickness of the window material to handle the extra force from the atmospheric pressure. Thicker windows cause more transmission loss at ambient temperatures, increasing optical loading and decreasing sensitivity. We have developed the use of woven High Modulus Polyethylene (HMPE), a material 100 times stronger than HDPE, to manufacture stronger, thinner windows using a pressurized hot lamination process. We discuss the development of a specialty autoclave for generating thin laminate vacuum windows and the optical and mechanical characterization of full scale science grade windows, with the goal of developing a new window suitable for BICEP Array cryostats and for future CMB applications

Enriquecimiento a gran escala y caracterización cibernética estadística del tráfico de red

Modern network sensors continuously produce enormous quantities of raw data that are beyond the capacity of human analysts. Cross-correlation of network sensors increases this challenge by enriching every network event with additional metadata. These large volumes of enriched network data present opportunities to statistically characterize network traffic and quickly answer a key question: 'What are the primary cyber characteristics of my network data?' The Python GraphBLAS and PyD4M analysis frameworks enable anonymized statistical analysis to be performed quickly and efficiently on very large network data sets. This approach is tested using billions of anonymized network data samples from the largest Internet observatory (CAIDA Telescope) and tens of millions of anonymized records from the largest commercially available background enrichment capability (GreyNoise). The analysis confirms that most of the enriched variables follow expected heavy-tail distributions and that a large fraction of the network traffic is due to a small number of cyber activities. This information can simplify the cyber analysts' task by enabling prioritization of cyber activities based on statistical prevalence. Los sensores de red modernos producen enormes cantidades de datos sin procesar que están más allá de la capacidad del análisis humano. Una correlación cruzada de sensores de red se convierte en un desafío al enriquecer cada evento de red con metadatos adicionales. Estos grandes volúmenes de datos de red enriquecidos presentan una oportunidad para caracterizar estadísticamente el tráfico de red y responder a la pregunta: "¿Cuáles son las principales características cibernéticas de mis datos de red?" Los esquemas de análisis de Python GraphBLAS y D4M permiten realizar análisis estadísticos anónimos, rápidos y eficientes en conjuntos grandes de datos de red. Este enfoque se prueba utilizando miles de millones de muestras de datos de red anónimos del observatorio de Internet más grande (Telescopio CAIDA) y decenas de millones de registros anónimos del fondo comercial con la mayor capacidad de enriquecimiento (GreyNoise). El análisis confirma que la mayoría de las variables enriquecidas siguen las distribuciones de cola pesada y que una gran fracción del tráfico de red se debe a una pequeña cantidad de actividades cibernéticas. Esta información puede simplificar la tarea de los analistas cibernéticos al permitir la priorización de las actividades cibernéticas en función de la prevalencia estadística.National Science FoundationImmediate accessThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Visibility Matters 2010: Higher Education and Teacher/Social Work Preparation in Illinois – A Web-based Assessment of LGBTQ Presence

How visible are lesbian, gay, bisexual, transgender, queer/questioning (LGBTQ) issues in programs that prepare educators and social workers to work in schools across Illinois? Which institutions include sexual orientation and gender identity in their policies? Are sexual orientation and gender identity identified in teacher and social work education programs' conceptual frameworks? The Pre-Professional Preparation Project (P-Project) seeks to answer these questions and to report the results via the Visibility Matters report cards. Using only data available from university and college websites, the Visibility Matters report cards evaluate the public face of pre-professional programs across Illinois. This paper describes the project's rationale and goals, history, current status, and potential future directions