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.

The estimation of energy demand (by power plants) has traditionally relied on historical energy use data for the region(s) that a plant produces for. Regression analysis, artificial neural network and Bayesian theory are the most common approaches for analysing these data. Such data and techniques do not generate reliable results. Consequently, excess energy has to be generated to prevent blackout; causes for energy surge are not easily determined; and potential energy use reduction from energy efficiency solutions is usually not translated into actual energy use reduction. The paper highlights the weaknesses of traditional techniques, and lays out a framework to improve the prediction of energy demand by combining energy use models of equipment, physical systems and buildings, with the proposed data mining algorithms for reverse engineering. The research team first analyses data samples from large complex energy data, and then, presents a set of computationally efficient data mining algorithms for reverse engineering. In order to develop a structural system model for reverse engineering, two focus groups are developed that has direct relation with cause and effect variables. The research findings of this paper includes testing out different sets of reverse engineering algorithms, understand their output patterns and modify algorithms to elevate accuracy of the outputs.

Small and medium office buildings consume a significant parcel of the U.S. building stock energy consumption. Still, owners lack resources and experience to conduct detailed energy audits and retrofit analysis. We present an eight-steps framework for an energy retrofit assessment in small and medium office buildings. Through a bottom-up approach and a web-based retrofit toolkit tested on a case study in Arizona, this methodology was able to save about 50% of the total energy consumed by the case study building, depending on the adopted measures and invested capital. While the case study presented is a deep energy retrofit, the proposed framework is effective in guiding the decision-making process that precedes any energy retrofit, deep or light.

Commercial buildings’ consumption is driven by multiple factors that include occupancy, system and equipment efficiency, thermal heat transfer, equipment plug loads, maintenance and operational procedures, and outdoor and indoor temperatures. A modern building energy system can be viewed as a complex dynamical system that is interconnected and influenced by external and internal factors. Modern large scale sensor measures some physical signals to monitor real-time system behaviors. Such data has the potentials to detect anomalies, identify consumption patterns, and analyze peak loads. The paper proposes a novel method to detect hidden anomalies in commercial building energy consumption system. The framework is based on Hilbert-Huang transform and instantaneous frequency analysis. The objectives are to develop an automated data pre-processing system that can detect anomalies and provide solutions with real-time consumption database using Ensemble Empirical Mode Decomposition (EEMD) method. The finding of this paper will also include the comparisons of Empirical mode decomposition and Ensemble empirical mode decomposition of three important type of institutional buildings.

There are many data mining and machine learning techniques to manage large sets of complex energy supply and demand data for building, organization and city. As the amount of data continues to grow, new data analysis methods are needed to address the increasing complexity. Using data from the energy loss between the supply (energy production sources) and demand (buildings and cities consumption), this paper proposes a Semi-Supervised Energy Model (SSEM) to analyse different loss factors for a building cluster. This is done by deep machine learning by training machines to semi-supervise the learning, understanding and manage the process of energy losses. Semi-Supervised Energy Model (SSEM) aims at understanding the demand-supply characteristics of a building cluster and utilizes the confident unlabelled data (loss factors) using deep machine learning techniques. The research findings involves sample data from one of the university campuses and presents the output, which provides an estimate of losses that can be reduced. The paper also provides a list of loss factors that contributes to the total losses and suggests a threshold value for each loss factor, which is determined through real time experiments. The conclusion of this paper provides a proposed energy model that can provide accurate numbers on energy demand, which in turn helps the suppliers to adopt such a model to optimize their supply strategies.

Background: A limitation of traditional outcome studies from behavioral interventions is the lack of attention given to evaluating the influence of moderating variables. This study examined possible moderation effect of baseline activity levels on physical activity change as a result of the Ready for Recess intervention.

Methods: Ready for Recess (August 2009-September 2010) was a controlled trial with twelve schools randomly assigned to one of four conditions: control group, staff supervision, equipment availability, and the combination of staff supervision and equipment availability. A total of 393 children (181 boys and 212 girls) from grades 3 through 6 (8–11 years old) were asked to wear an Actigraph monitor during school time on 4–5 days of the week. Assessments were conducted at baseline (before intervention) and post intervention (after intervention).

Results: Initial MVPA moderated the effect of Staff supervision (β = −0.47%; p < .05), but not Equipment alone and Staff + Equipment (p > .05). Participants in the Staff condition that were 1 standard deviation (SD) below the mean for baseline MVPA (classified as “low active”) had lower MVPA levels at post-intervention when compared with their low active peers in the control condition (Mean_diff = −10.8 ± 2.9%; p = .005). High active individuals (+1SD above the mean) in the Equipment treatment also had lower MVPA values at post-intervention when compared with their highly active peers in the control group (Mean_diff = −9.5 ± 2.9%; p = .009).

Conclusions: These results indicate that changes in MVPA levels at post-intervention were reduced in highly active participants when recess staff supervision was provided. In this study, initial MVPA moderated the effect of Staff supervision on children’s MVPA after 6 months of intervention. Staff training should include how to work with inactive youth but also how to assure that active children remain active.

Background: In the United States, approximately one in 110 pregnancies end in stillbirth affecting more than 26,000 women annually. Women experiencing stillbirth have a threefold greater risk of developing depressive symptoms compared to women experiencing live birth. Depression contributes negatively to health outcomes for both mothers and babies subsequent to stillbirth. Physical activity may improve depression in these women, however, little is known about acceptable physical activity interventions for women after stillbirth. This is the purpose of this descriptive exploratory study.

Methods: Eligible women were between ages 19 and 45, and experienced stillbirth within one year of the study. An online survey was used to ask questions related to 1) pregnancy and family information (i.e., time since stillbirth, weight gain during pregnancy, number of other children) 2) physical activity participation, 3) depressive symptomatology, and 4) demographics.

Results: One hundred seventy-five women participated in the study (M age = 31.26 ± 5.52). Women reported participating in regular physical activity (at least 150 minutes of moderate activity weekly) before (60%) and during (47%) their pregnancy, as well as after their stillbirth (61%). Only 37% were currently meeting physical activity recommendations. Approximately 88% reported depression (i.e., score of >10 on depression scale). When asked how women cope with depression, anxiety, or grief, 38% said physical activity. Of those that reported using physical activity to cope after stillbirth, they did so to help with depression (58%), weight loss (55%), and better overall physical health (52%). To cope with stillbirth, women used walking (67%), followed by jogging (35%), and yoga (23%). Women who participated in physical activity after stillbirth reported significantly lower depressive symptoms (M = 15.10, SD = 5.32) compared to women who did not participate in physical activity (M = 18.06, SD = 5.57; t = -3.45, p = .001).

Conclusions: Physical activity may serve as a unique opportunity to help women cope with the multiple mental sequelae after stillbirth. This study provides data to inform healthcare providers about the potential role of physical activity in bereavement and recovery for women who have experienced stillbirth. Additional research is necessary in this vulnerable population.

Background: The transition to parenthood is consistently associated with declines in physical activity. In particular, working parents are at risk for inactivity, but research exploring physical activity barriers and facilitators in this population has been scarce. The purpose of this study was to qualitatively examine perceptions of physical activity among working parents.

Methods: Working mothers (n = 13) and fathers (n = 12) were recruited to participate in one of four focus group sessions and discuss physical activity barriers and facilitators. Data were analyzed using immersion/crystallization in NVivo 10.

Results: Major themes for barriers included family responsibilities, guilt, lack of support, scheduling constraints, and work. Major themes for facilitators included being active with children or during children’s activities, being a role model for children, making time/prioritizing, benefits to health and family, and having support available. Several gender differences emerged within each theme, but overall both mothers and fathers reported their priorities had shifted to focus on family after becoming parents, and those who were fitting in physical activity had developed strategies that allowed them to balance their household and occupational responsibilities.

Conclusions: The results of this study suggest working mothers and fathers report similar physical activity barriers and facilitators and would benefit from interventions that teach strategies for overcoming barriers and prioritizing physical activity amidst the demands of parenthood. Future interventions might consider targeting mothers and fathers in tandem to create an optimally supportive environment in the home.

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.