arXiv subjects

Andrew Gunn

Publications and source records attributed to Andrew Gunn.

3 recordsLinked to original sources

arXiv2020

Spatial and temporal development of incipient dunes

In zones of loose sand, wind-blown sand dunes emerge due the linear instability of a flat sedimentary bed. This instability has been studied in experiments and numerical models but rarely in the field, due to the large time and length scales involved. We examine dune formation at the upwind margin of the White Sands Dune Field in New Mexico (USA), using 4 years of lidar topographic data to follow the spatial and temporal development of incipient dunes. Data quantify dune wavelength, growth rate, and propagation velocity and also the characteristic length scale associated with the growth process. We show that all these measurements are in quantitative agreement with predictions from linear stability analysis. This validation makes it possible to use the theory to reliably interpret dune-pattern characteristics and provide quantitative constraints on associated wind regimes and sediment properties, where direct local measurements are not available or feasible.

physics.geo-ph↗

arXiv2020

Circadian rhythm of dune-field activity

Wind-blown sand dunes are both a consequence and a driver of climate dynamics; they arise under persistently dry and windy conditions, and are sometimes a source for airborne dust. Dune fields experience extreme daily changes in temperature, yet the role of atmospheric stability in driving sand transport and dust emission has not been established. Here we report on an unprecedented multi-scale field experiment at the White Sands Dune Field (New Mexico, USA), where we demonstrate that a daily rhythm of sand and dust transport arises from non-equilibrium atmospheric boundary layer convection. A global analysis of 45 dune fields confirms the connection between surface wind speed and diurnal temperature cycles, revealing an unrecognized climate feedback that may contribute to the growth of deserts on Earth and dune activity on Mars.

physics.geo-ph↗

arXiv2016

Darknet and Deepnet Mining for Proactive Cybersecurity Threat Intelligence

In this paper, we present an operational system for cyber threat intelligence gathering from various social platforms on the Internet particularly sites on the darknet and deepnet. We focus our attention to collecting information from hacker forum discussions and marketplaces offering products and services focusing on malicious hacking. We have developed an operational system for obtaining information from these sites for the purposes of identifying emerging cyber threats. Currently, this system collects on average 305 high-quality cyber threat warnings each week. These threat warnings include information on newly developed malware and exploits that have not yet been deployed in a cyber-attack. This provides a significant service to cyber-defenders. The system is significantly augmented through the use of various data mining and machine learning techniques. With the use of machine learning models, we are able to recall 92% of products in marketplaces and 80% of discussions on forums relating to malicious hacking with high precision. We perform preliminary analysis on the data collected, demonstrating its application to aid a security expert for better threat analysis.

cs.CR↗