DeepAPT: Nation-State APT Attribution Using End-to-End Deep Neural Networks

In recent years numerous advanced malware, aka advanced persistent threats (APT) are allegedly developed by nation-states. The task of attributing an APT to a specific nation-state is extremely challenging for several reasons. Each nation-state has usually more than a single cyber unit that develops such advanced malware, rendering traditional authorship attribution algorithms useless. Furthermore, those APTs use state-of-the-art evasion techniques, making feature extraction challenging. Finally, the dataset of such available APTs is extremely small. In this paper we describe how deep neural networks (DNN) could be successfully employed for nation-state APT attribution. We use sandbox reports (recording the behavior of the APT when run dynamically) as raw input for the neural network, allowing the DNN to learn high level feature abstractions of the APTs itself. Using a test set of 1,000 Chinese and Russian developed APTs, we achieved an accuracy rate of 94.6%

Predation risk by largemouth bass modulates feeding functional responses of native and non-native crayfish

Context-dependency is prevalent in nature, challenging our understanding and prediction of the potential ecological impacts of non-native species (NNS). The presence of a top predator, for example, can modify the foraging behaviour of an intermediate consumer, by means of non-consumptive effects. This raises the question of whether the fear of predation might modulate consumption rates of NNS, thus shaping the magnitude of ecological impacts. Here, we quantified the functional feeding responses of three non-native crayfish species – red swamp crayfish Procambarus clarkii, rusty crayfish Faxonius rusticus and virile crayfish Faxonius virilis – compared to the native analogue signal crayfish Pacifastacus leniusculus, considering the predation risk imposed by a top fish predator, the globally invasive largemouth bass Micropterus salmoides. We applied the comparative functional response (FR) approach using snails as prey and exposing crayfish to water containing predator and dietary chemical cues or not. All crayfish species presented a destabilising Type II FR, regardless of the presence of chemical cues. Predation risk resulted in significantly longer handling times or lower attack rates in non-native crayfish; however, no significant differences were observed in signal crayfish. We estimated per capita impacts for each species using the functional response ratio (FRR; attack rate divided by handling time). The FRR metric was lower for all crayfish species when exposed to predation risk. Rusty crayfish demonstrated the highest FRR in the absence of chemical cues, followed by signal crayfish, virile crayfish and red swamp crayfish. By contrast, the FRR of signal crayfish was nearly twice that of rusty crayfish and virile crayfish and ten times greater than red swamp crayfish when chemical cues were present. The latter result agrees with the well-recognised ecological impacts of signal crayfish throughout its globally-introduced range. This study demonstrates the importance of considering the non-consumptive effects of predators when quantifying the ecological impacts of intermediate non-native consumers on prey. The direction and magnitude of the modulating effects of predators have clear implications for our understanding of NNS impacts and the prioritisation of management actions

The effect of spatial configuration of habitat capacity on β diversity

Patterns of β diversity are commonly used to infer underlying ecological processes. In this study, we examined the effect of spatial configuration of habitat capacity on different metrics of β diversity, i.e., β diversity measured as turnover and as variation. For β diversity as turnover, a monotonic species spatial turnover pattern is typically considered as a benchmark for species distributions driven only by dispersal process. Deviations from a monotonic curve are attributed to local environmental filtering (i.e., the same environmental factors affecting different species differently). However, we found non-monotonicity in species spatial turnover in models without environmental filtering effect. This non-monotonicity was caused by variation in α diversity, introduced by spatial configuration of habitat capacity. After applying a recent null-model approach—designed to tease out the effect of variation in α diversity—species spatial turnover remained non-monotonic. This non-monotonicity makes it problematic to use species spatial turnover to infer the underlying processes for species distribution, i.e., whether it is driven by environmental filtering or dispersal processes. Spatial configuration of habitat capacity also influences landscape connectivity. Small-habitat capacity sites may constrain movements of organisms (i.e., dispersal) between sites supporting high capacity habitats. We showed that in a landscape where small-habitat capacity sites were located in positions important for dispersal (e.g., in the center as opposed to on the edge of a landscape) has a higher spatial variation of species composition, hence, higher β diversity. Ecologists who use different measures of β diversity should be aware of these effects introduced by spatial configuration of habitat capacity

Stratified dispersal and increasing genetic variation during the invasion of Central Europe by the western corn rootworm, Diabrotica virgifera virgifera

Invasive species provide opportunities for investigating evolutionary aspects of colonization processes, including initial foundations of populations and geographic expansion. Using microsatellite markers and historical information, we characterized the genetic patterns of the invasion of the western corn rootworm (WCR), a pest of corn crops, in its largest area of expansion in Europe: Central and South-Eastern (CSE) Europe. We found that the invaded area probably corresponds to a single expanding population resulting from a single introduction of WCR and that gene flow is geographically limited within the population. In contrast to what is expected in classical colonization processes, an increase in genetic variation was observed from the center to the edge of the outbreak. Control measures against WCR at the center of the outbreak may have decreased effective population size in this area which could explain this observed pattern of genetic variation. We also found that small remote outbreaks in southern Germany and north-eastern Italy most likely originated from long-distance dispersal events from CSE Europe. We conclude that the large European outbreak is expanding by stratified dispersal, involving both continuous diffusion and discontinuous long-distance dispersal. This latter mode of dispersal may accelerate the expansion of WCR in Europe in the future

2015 AAPP Monograph Series: African American Professors Program

The African American Professors Program (AAPP) at the University of South Carolina is proud to publish its fourteenth edition of this annual monograph series. AAPP recognizes the significance of offering its scholars a venue on which to engage actively in research and to publish their refereed papers. Parallel with the publication of their refereed manuscripts is the opportunity to gain visibility among scholars throughout institutions in national and international settings. Scholars who have contributed papers for this monograph are acknowledged for embracing the value of including this responsibility within their academic milieu. Writing across disciplines adds to the intellectual diversity of these manuscripts. From neophytes to quite experienced individuals, the chapters have been researched and comprehensively written. Founded in 1997 through the Department of Educational Leadership and Policies in the College of Education, AAPP was designed to address the under-representation of African American professors on college and university campuses. Its mission is to expand the pool of these professors in critical academic and research areas. Sponsored by the University of South Carolina, the W.K. Kellogg Foundation, and the South Carolina General Assembly, the program recruits doctoral students for disciplines in which African Americans currently are underrepresented among faculty in higher education. The continuation of this monograph series is seen as responding to a window of opportunity to be sensitive to an academic expectation of graduates as they pursue career placement and, at the same time, to allow for the dissemination of products of scholarship to a broader community. The importance of this monograph series has been voiced by one of our 2002 AAPP graduates, Dr. Shundele LaTjuan Dogan, formerly an Administrative Fellow at Harvard University, a Program Officer for the Southern Education Foundation, and a Program Officer for the Arthur M. Blank Foundation in Atlanta, Georgia. She is currently a Corporate Citizenship and Corporate Affairs Manager for IBM-International Business Machines in Atlanta, Georgia and has written the Foreword for the 2014 monograph. Dr. Dogan wrote: One thing in particular that I want to thank you for is having the African American Professors Program scholars publish articles for the monograph. I have to admit that writing the articles seemed like extra work at the time. However, in my recent interview process, organizations have asked me for samples of my writing. Including an article from a published monograph helped to make my portfolio much more impressive. You were \u27right on target\u27 in having us do the monograph series. (AAPP 2003 Monograph, p. xi) The African American Professors Program continues its tradition as a promoter of scholarship in higher education as evidenced through the inspiration from this group of interdisciplinary manuscripts. As we embark on a new phase of development by initiating the renaming of our program, the Carolina Diversity Professors Program, we are grateful for your continued interest and support of the work of the scholars. In conclusion, I hope that you will envision these published papers as serving as an invaluable contribution to your own professional and career development. John McFadden, Ph.D. The Benjamin Elijah Mays Distinguished Professor Emeritus Director, African American Professors Program University of South Carolinahttps://scholarcommons.sc.edu/mcfadden_monographs/1002/thumbnail.jp

A novel approach for measuring residential socioeconomic factors associated with cardiovascular and metabolic health

Individual-level characteristics, including socioeconomic status, have been associated with poor metabolic and cardiovascular health; however, residential area-level characteristics may also independently contribute to health status. In the current study, we used hierarchical clustering to aggregate 444 US Census block groups in Durham, Orange, and Wake Counties, NC, USA into six homogeneous clusters of similar characteristics based on 12 demographic factors. We assigned 2254 cardiac catheterization patients to these clusters based on residence at first catheterization. After controlling for individual age, sex, smoking status, and race, there were elevated odds of patients being obese (odds ratio (OR) = 1.92, 95% confidence intervals (CI) = 1.39, 2.67), and having diabetes (OR = 2.19, 95% CI = 1.57, 3.04), congestive heart failure (OR = 1.99, 95% CI = 1.39, 2.83), and hypertension (OR = 2.05, 95% CI = 1.38, 3.11) in a cluster that was urban, impoverished, and unemployed, compared with a cluster that was urban with a low percentage of people that were impoverished or unemployed. Our findings demonstrate the feasibility of applying hierarchical clustering to an assessment of area-level characteristics and that living in impoverished, urban residential clusters may have an adverse impact on health

A Neural Approach to Ordinal Regression for the Preventive Assessment of Developmental Dyslexia

Developmental Dyslexia (DD) is a learning disability related to the acquisition of reading skills that affects about 5% of the population. DD can have an enormous impact on the intellectual and personal development of affected children, so early detection is key to implementing preventive strategies for teaching language. Research has shown that there may be biological underpinnings to DD that affect phoneme processing, and hence these symptoms may be identifiable before reading ability is acquired, allowing for early intervention. In this paper we propose a new methodology to assess the risk of DD before students learn to read. For this purpose, we propose a mixed neural model that calculates risk levels of dyslexia from tests that can be completed at the age of 5 years. Our method first trains an auto-encoder, and then combines the trained encoder with an optimized ordinal regression neural network devised to ensure consistency of predictions. Our experiments show that the system is able to detect unaffected subjects two years before it can assess the risk of DD based mainly on phonological processing, giving a specificity of 0.969 and a correct rate of more than 0.92. In addition, the trained encoder can be used to transform test results into an interpretable subject spatial distribution that facilitates risk assessment and validates methodology.Comment: 12 pages, 4 figure