ASU Scholarship Showcase

A relatively unexplored issue in cybersecurity science and engineering is whether there exist intrinsic patterns of cyberattacks. Conventional wisdom favors absence of such patterns due to the overwhelming complexity of the modern cyberspace. Surprisingly, through a detailed analysis of an extensive data set that records the time-dependent frequencies of attacks over a relatively wide range of consecutive IP addresses, we successfully uncover intrinsic spatiotemporal patterns underlying cyberattacks, where the term “spatio” refers to the IP address space. In particular, we focus on analyzing macroscopic properties of the attack traffic flows and identify two main patterns with distinct spatiotemporal characteristics: deterministic and stochastic. Strikingly, there are very few sets of major attackers committing almost all the attacks, since their attack “fingerprints” and target selection scheme can be unequivocally identified according to the very limited number of unique spatiotemporal characteristics, each of which only exists on a consecutive IP region and differs significantly from the others. We utilize a number of quantitative measures, including the flux-fluctuation law, the Markov state transition probability matrix, and predictability measures, to characterize the attack patterns in a comprehensive manner. A general finding is that the attack patterns possess high degrees of predictability, potentially paving the way to anticipating and, consequently, mitigating or even preventing large-scale cyberattacks using macroscopic approaches.

Supply-demand processes take place on a large variety of real-world networked systems ranging from power grids and the internet to social networking and urban systems. In a modern infrastructure, supply-demand systems are constantly expanding, leading to constant increase in load requirement for resources and consequently, to problems such as low efficiency, resource scarcity, and partial system failures. Under certain conditions global catastrophe on the scale of the whole system can occur through the dynamical process of cascading failures. We investigate optimization and resilience of time-varying supply-demand systems by constructing network models of such systems, where resources are transported from the supplier sites to users through various links. Here by optimization we mean minimization of the maximum load on links, and system resilience can be characterized using the cascading failure size of users who fail to connect with suppliers.

We consider two representative classes of supply schemes: load driven supply and fix fraction supply. Our findings are: (1) optimized systems are more robust since relatively smaller cascading failures occur when triggered by external perturbation to the links; (2) a large fraction of links can be free of load if resources are directed to transport through the shortest paths; (3) redundant links in the performance of the system can help to reroute the traffic but may undesirably transmit and enlarge the failure size of the system; (4) the patterns of cascading failures depend strongly upon the capacity of links; (5) the specific location of the trigger determines the specific route of cascading failure, but has little effect on the final cascading size; (6) system expansion typically reduces the efficiency; and (7) when the locations of the suppliers are optimized over a long expanding period, fewer suppliers are required. These results hold for heterogeneous networks in general, providing insights into designing optimal and resilient complex supply-demand systems that expand constantly in time.

Plasmodium vivax is the most prevalent malarial species in South America and exerts a substantial burden on the populations it affects. The control and eventual elimination of P. vivax are global health priorities. Genomic research contributes to this objective by improving our understanding of the biology of P. vivax and through the development of new genetic markers that can be used to monitor efforts to reduce malaria transmission. Here we analyze whole-genome data from eight field samples from a region in Cordóba, Colombia where malaria is endemic. We find considerable genetic diversity within this population, a result that contrasts with earlier studies suggesting that P. vivax had limited diversity in the Americas. We also identify a selective sweep around a substitution known to confer resistance to sulphadoxine-pyrimethamine (SP). This is the first observation of a selective sweep for SP resistance in this species. These results indicate that P. vivax has been exposed to SP pressure even when the drug is not in use as a first line treatment for patients afflicted by this parasite. We identify multiple non-synonymous substitutions in three other genes known to be involved with drug resistance in Plasmodium species. Finally, we found extensive microsatellite polymorphisms. Using this information we developed 18 polymorphic and easy to score microsatellite loci that can be used in epidemiological investigations in South America.

Inbreeding in hermaphroditic plants can occur through two different mechanisms: biparental inbreeding, when a plant mates with a related individual, or self-fertilization, when a plant mates with itself. To avoid inbreeding, many hermaphroditic plants have evolved self-incompatibility (SI) systems which prevent or limit self-fertilization. One particular SI system—homomorphic SI—can also reduce biparental inbreeding. Homomorphic SI is found in many angiosperm species, and it is often assumed that the additional benefit of reduced biparental inbreeding may be a factor in the success of this SI system. To test this assumption, we developed a spatially-explicit, individual-based simulation of plant populations that displayed three different types of homomorphic SI. We measured the total level of inbreeding avoidance by comparing each population to a self-compatible population (NSI), and we measured biparental inbreeding avoidance by comparing to a population of self-incompatible plants that were free to mate with any other individual (PSI).

Because biparental inbreeding is more common when offspring dispersal is limited, we examined the levels of biparental inbreeding over a range of dispersal distances. We also tested whether the introduction of inbreeding depression affected the level of biparental inbreeding avoidance. We found that there was a statistically significant decrease in autozygosity in each of the homomorphic SI populations compared to the PSI population and, as expected, this was more pronounced when seed and pollen dispersal was limited. However, levels of homozygosity and inbreeding depression were not reduced. At low dispersal, homomorphic SI populations also suffered reduced female fecundity and had smaller census population sizes. Overall, our simulations showed that the homomorphic SI systems had little impact on the amount of biparental inbreeding in the population especially when compared to the overall reduction in inbreeding compared to the NSI population. With further study, this observation may have important consequences for research into the origin and evolution of homomorphic self-incompatibility systems.

Background: Blindness has evolved repeatedly in cave-dwelling organisms, and many hypotheses have been proposed to explain this observation, including both accumulation of neutral loss-of-function mutations and adaptation to darkness. Investigating the loss of sight in cave dwellers presents an opportunity to understand the operation of fundamental evolutionary processes, including drift, selection, mutation, and migration.

Results: Here we model the evolution of blindness in caves. This model captures the interaction of three forces: (1) selection favoring alleles causing blindness, (2) immigration of sightedness alleles from a surface population, and (3) mutations creating blindness alleles. We investigated the dynamics of this model and determined selection-strength thresholds that result in blindness evolving in caves despite immigration of sightedness alleles from the surface. We estimate that the selection coefficient for blindness would need to be at least 0.005 (and maybe as high as 0.5) for blindness to evolve in the model cave-organism, Astyanax mexicanus.

Conclusions: Our results indicate that strong selection is required for the evolution of blindness in cave-dwelling organisms, which is consistent with recent work suggesting a high metabolic cost of eye development.

Most previous works on complete synchronization of chaotic oscillators focused on the one-channel interaction scheme where the oscillators are coupled through only one variable or a symmetric set of variables. Using the standard framework of master-stability function (MSF), we investigate the emergence of complex synchronization behaviors under all possible configurations of two-channel coupling, which include, for example, all possible cross coupling schemes among the dynamical variables. Utilizing the classic Rössler and Lorenz oscillators, we find a rich variety of synchronization phenomena not present in any previously extensively studied, single-channel coupling configurations. For example, in many cases two coupling channels can enhance or even generate synchronization where there is only weak or no synchronization under only one coupling channel, which has been verified in a coupled neuron system. There are also cases where the oscillators are originally synchronized under one coupling channel, but an additional synchronizable coupling channel can, however, destroy synchronization. Direct numerical simulations of actual synchronization dynamics verify the MSF-based predictions. Our extensive computation and heuristic analysis provide an atlas for synchronization of chaotic oscillators coupled through two channels, which can be used as a systematic reference to facilitate further research in this area.

Background: Medical and public health scientists are using evolution to devise new strategies to solve major health problems. But based on a 2003 survey, medical curricula may not adequately prepare physicians to evaluate and extend these advances. This study assessed the change in coverage of evolution in North American medical schools since 2003 and identified opportunities for enriching medical education.

Methods: In 2013, curriculum deans for all North American medical schools were invited to rate curricular coverage and perceived importance of 12 core principles, the extent of anticipated controversy from adding evolution, and the usefulness of 13 teaching resources. Differences between schools were assessed by Pearson’s chi-square test, Student’s t-test, and Spearman’s correlation. Open-ended questions sought insight into perceived barriers and benefits.

Results: Despite repeated follow-up, 60 schools (39%) responded to the survey. There was no evidence of sample bias. The three evolutionary principles rated most important were antibiotic resistance, environmental mismatch, and somatic selection in cancer. While importance and coverage of principles were correlated (r = 0.76, P < 0.01), coverage (at least moderate) lagged behind importance (at least moderate) by an average of 21% (SD = 6%). Compared to 2003, a range of evolutionary principles were covered by 4 to 74% more schools. Nearly half (48%) of responders anticipated igniting controversy at their medical school if they added evolution to their curriculum. The teaching resources ranked most useful were model test questions and answers, case studies, and model curricula for existing courses/rotations. Limited resources (faculty expertise) were cited as the major barrier to adding more evolution, but benefits included a deeper understanding and improved patient care.

Conclusion: North American medical schools have increased the evolution content in their curricula over the past decade. However, coverage is not commensurate with importance. At a few medical schools, anticipated controversy impedes teaching more evolution. Efforts to improve evolution education in medical schools should be directed toward boosting faculty expertise and crafting resources that can be easily integrated into existing curricula.

Under models of isolation-by-distance, population structure is determined by the probability of identity-by-descent between pairs of genes according to the geographic distance between them. Well established analytical results indicate that the relationship between geographical and genetic distance depends mostly on the neighborhood size of the population which represents a standardized measure of gene flow. To test this prediction, we model local dispersal of haploid individuals on a two-dimensional landscape using seven dispersal kernels: Rayleigh, exponential, half-normal, triangular, gamma, Lomax and Pareto. When neighborhood size is held constant, the distributions produce similar patterns of isolation-by-distance, confirming predictions. Considering this, we propose that the triangular distribution is the appropriate null distribution for isolation-by-distance studies. Under the triangular distribution, dispersal is uniform over the neighborhood area which suggests that the common description of neighborhood size as a measure of an effective, local panmictic population is valid for popular families of dispersal distributions. We further show how to draw random variables from the triangular distribution efficiently and argue that it should be utilized in other studies in which computational efficiency is important.

Resource allocation takes place in various types of real-world complex systems such as urban traffic, social services institutions, economical and ecosystems. Mathematically, the dynamical process of resource allocation can be modeled as minority games. Spontaneous evolution of the resource allocation dynamics, however, often leads to a harmful herding behavior accompanied by strong fluctuations in which a large majority of agents crowd temporarily for a few resources, leaving many others unused. Developing effective control methods to suppress and eliminate herding is an important but open problem. Here we develop a pinning control method, that the fluctuations of the system consist of intrinsic and systematic components allows us to design a control scheme with separated control variables. A striking finding is the universal existence of an optimal pinning fraction to minimize the variance of the system, regardless of the pinning patterns and the network topology. We carry out a generally applicable theory to explain the emergence of optimal pinning and to predict the dependence of the optimal pinning fraction on the network topology. Our work represents a general framework to deal with the broader problem of controlling collective dynamics in complex systems with potential applications in social, economical and political systems.

Successful identification of directed dynamical influence in complex systems is relevant to significant problems of current interest. Traditional methods based on Granger causality and transfer entropy have issues such as difficulty with nonlinearity and large data requirement. Recently a framework based on nonlinear dynamical analysis was proposed to overcome these difficulties. We find, surprisingly, that noise can counterintuitively enhance the detectability of directed dynamical influence. In fact, intentionally injecting a proper amount of asymmetric noise into the available time series has the unexpected benefit of dramatically increasing confidence in ascertaining the directed dynamical influence in the underlying system. This result is established based on both real data and model time series from nonlinear ecosystems. We develop a physical understanding of the beneficial role of noise in enhancing detection of directed dynamical influence.