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Title: SELECTION TEST SECURITY: ARE SIMULATIONS MORE SUSCEPTIBLE TO TEST SECURITY CONCERNS THAN TRADITIONAL ASSESSMENTS?
Creator: Daisley, Rebecca Roller
Date: 2015, 2015-12
Description: The current study examined whether information about the job simulation portion of a selection assessment battery is more susceptible to a...
Show moreThe current study examined whether information about the job simulation portion of a selection assessment battery is more susceptible to a test security threat than more traditional assessments, where test security is threatened when assessment content is shared. Participants were asked to play the role of a job candidate for a customer service representative. They were given three assessments: a cognitive ability test, a personality inventory, and a job simulation. After completing the assessments, participants were asked to write an e-mail to a hypothetical friend who will also be applying for the job, and include any information that would help their friend in the application process. It was hypothesized that the most information and the most useful information shared by participants would be about the job simulation portion of the assessment battery. The findings supported the hypotheses, suggesting that job simulations are more susceptible to information sharing by applicants than the other assessments, and therefore are exposed to a greater test security threat. The discussion includes implications for practice.
Ph.D. in Psychology, December 2015
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Title: AGENT-BASED MODELING OF IMMUNE RESPONSE IN THE DEVELOPMENT OF TYPE 1 DIABETES
Creator: Xu, Qian
Date: 2020
Description: Diabetes is a chronic disease that affects a large number of people around the world and cause many co-morbidities ranging from cardiovascular...
Show moreDiabetes is a chronic disease that affects a large number of people around the world and cause many co-morbidities ranging from cardiovascular diseases, neuropathy, retinopathy and blindness and kidney failure. The economic burden induced by diabetes is not only caused by the wage loss and medical burden, but also with the cost of treatment of diabetes and co-morbidities caused by diabetes. Clinical research for treatment and cure of diabetes is costly. Computer modeling and simulation studies provide an economical alternative to conduct preliminary evaluation of new hypotheses and alternatives in new therapies. The most promising results obtained from simulations can then be investigates experimentally, improving the efficiency of experiments and clinical studies. This work focuses on the development of an agent-based model to describe the destruction of islets and β cells and the development of Type 1 diabetes. The whole process of inflammation related to diabetes takes place in pancreatic lymph node, circulation, and pancreatic tissue with islets. The infiltration to islets and insulin-producing β cell damage happens in the pancreatic tissue with islets; the lymphocytes activation and antigen presentation majorly happened in the pancreatic lymph node. Therefore, the model described activities taking place in the islets in the pancreatic tissue section and pancreatic lymph nodes, the interactions among T cells, α/β cells, antigen presentation cells and immunosuppression cells. Cell behavior was obtained from the literature that published experiment results and used to develop the rules followed by the agents representing various types of cells and their interactions. The agent-based model provides a framework to describe relationship between lymphocytes and β cell through the trends of cell variations in the inflammation and demonstrates the effects of these cells in the disease development. Two different systems, a mouse model and a human model have been developed. The simulation results with the mouse model indicate that the different types of regulatory cells play different roles in suppressing inflammation. Among them, the regulatory T cells play the most important role in suppressing inflammation, but the B regulatory cell conversion is the key to induce the cascade of regulatory cell generation in inflammatory environment when there are no regulatory cytokines in the environment. The simulation results with the human model are mostly similar with mouse model, however, their effect of potential therapies such as addition of Tregs did not do as well as that in mouse model. The treatment method might be adjusted by combining other cytokines or immunosuppression cells in human assays.
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Title: Combining Simulation and Emulation for Planning and Evaluation of Smart Grid Security, Resilience, and Operations
Creator: Hannon, Christopher
Date: 2020
Description: The modern power grid is a complex, large scale cyber-physical system comprising of generation, transmission and distribution elements....
Show moreThe modern power grid is a complex, large scale cyber-physical system comprising of generation, transmission and distribution elements. However, advancements in information technology have not yet caught up to the legacy operational technology used in the electric power system. Coupled with the proliferation of renewable energy sources, the electric power grid is in a transition to a smarter grid; operators are now being equipped with the tools to make real-time operational changes and the ability to monitor and provide situational awareness of the system. This shift in electric power grid priorities requires an expansive and reliable communication network to enhance efficiency and resilience of the Smart Grid. This trend calls for a simulation-based platform that provides sufficient flexibility and controllability for evaluating network application designs, and facilitating the transition from in-house research ideas into production systems. In this Thesis, I present techniques to efficiently combine simulation systems, emulation systems, and real hardware into testbed systems to evaluate security, resilience, and operations of the electric power grid. While simulating the dynamics of the physical components of the electric power grid, the cyber components including devices, applications, and networking functions are able to be emulated or even implemented using real hardware. In addition to novel synchronization algorithms between simulation and emulation systems, multiple test cases in applying software-defined networking, an emerging networking paradigm, to the power grid for security and resilience and phasor measurement unit analytics for grid operations are presented which motivate the need for a simulation-based testbed. The contributions of this work lay in the design of a virtual time system with tight controllability on the execution of the emulation systems, i.e., pausing and resuming any specified container processes in the perception of their own virtual clocks, and also lay in the distributed virtual time based synchronization across embedded Linux devices.
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Title: A SCALABLE SIMULATION AND MODELING FRAMEWORK FOR EVALUATION OF SOFTWARE-DEFINED NETWORKING DESIGN AND SECURITY APPLICATIONS
Creator: Yan, Jiaqi
Date: 2019
Description: The world today is densely connected by many large-scale computer networks, supporting military applications, social communications, power...
Show moreThe world today is densely connected by many large-scale computer networks, supporting military applications, social communications, power grid facilities, cloud services, and other critical infrastructures. However, a gap has grown between the complexity of the system and the increasing need for security and resilience. We believe this gap is now reaching a tipping point, resulting in a dramatic change in the way that networks and applications are architected, developed, monitored, and protected. This trend calls for a scalable and high-fidelity network testing and evaluation platform to facilitate the transformation from in-house research ideas to real-world working solutions. With this objective, we investigate means to build a scalable and high-fidelity network testbed using container-based emulation and parallel simulation; our study focuses on the emerging software-defined networking (SDN) technology. Existing evaluation platforms facilitate the adoption of the SDN architecture and applications to production systems. However, the performance of those platforms is highly dependent on the underlying physical hardware resources. Insufficient resources would lead to undesired results, such as low experimental fidelity or slow execution speed, especially with large-scale network settings. To improve the testbed fidelity, we first develop a lightweight virtual time system for Linux container and integrate the system into a widely-used SDN emulator. A key issue with an ordinary container-based emulator is that it uses the system clock across all the containers even if a container is not being scheduled to run, which leads to the issue of both performance and temporal fidelity, especially with high workloads. We investigate virtual time approaches by precisely scaling the time of interactions between containers and physical devices. Our evaluation results indicate a definite improvement in fidelity and scalability. To improve the testbed scalability, we investigate how the centralized paradigm of SDN can be utilized to reduce the simulation workload. We explore a model abstraction technique that effectively transforms the SDN network devices to one virtualized switch model. While significantly reducing the model execution time and enabling the real-time simulation capability, our abstracted model also preserves the end-to-end forwarding behavior of the original network.With enhanced fidelity and scalability, it is realistic to utilize our network testbed to perform a security evaluation of various SDN applications. We notice that the communication network generates and processes a huge amount of data. The logically-centralized SDN control plane, on the one hand, has to process both critical control traffic and potentially big data traffic, and on the other hand, enables many efficient security solutions, such as intrusion detection, mitigation, and prevention. Recently, deep neural networks achieve state-of-the-art results across a range of hard problem spaces. We study how to utilize the big data and deep learning to secure communication networks and host entities. For classifying malicious network traffic, we have performed the feasibility study of off-line deep-learning based intrusion detection by constructing the detection engine with multiple advanced deep learning models. For malware classification on individual hosts, another necessity to secure computer systems, existing machine learning-based malware classification methods rely on handcrafted features extracted from raw binary files or disassembled code. The diversity of such features created has made it hard to build generic malware classification systems that work effectively across different operational environments. To strike a balance between generality and performance, we explore new graph convolutional neural network techniques to effectively yet efficiently classify malware programs represented as their control flow graphs.
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Title: Comparison of an Ideal Point and Dominance IRT Model on the Detection of Differential Item Functioning with DFIT
Creator: Spizzuco Jr, Daniel
Date: 2019
Description: Item response theory (IRT) models can assume a variety of forms including,notably, dominance and ideal point-based probability distributions....
Show moreItem response theory (IRT) models can assume a variety of forms including,notably, dominance and ideal point-based probability distributions. But researchers haveonly recently begun to explore issues related to the above distinction. The current studytherefore examines whether model-data fit and rates of differential item functioning (DIF)detection remain comparable when data are analyzed via the ideal point-based generalizedgraded unfolding model (GGUM) vs. the dominance-based graded response model (GRM).To address these issues, item response data were simulated to contain dominance,ideal point and mixed response processes, and DIF and impact scenarios. Results indicatedthat model-data fit and DIF detection accuracy were not as closely aligned as anticipated.Overall, the GGUM fit data better than the GRM to the extent that any ideal point processeswere present, while the GRM was slightly better at fitting dominance-only data. With noimpact, however, the GGUM fit all embedded response data types better than the GRM.Results were mixed among impact scenarios. This pattern was found in both no DIF and DIFscenarios.Several points were made with respect to the DIF portion of the study. First, Type 1error rates were in most cases quite conservative for both models. Second, study-wide,more power emerged with dominance as compared to ideal point data for both models.Moreover, in no impact conditions, slightly more power accrued via the GGUM fordominance and ideal point data. With impact, however, the GRM produced somewhat morepower across data types. Third, in terms of DIF patterns/sources, power was high for bothmodels when DIF was embedded on the full set of location/threshold parameters, andlower with fewer differentially functioning (DF) location/threshold parameters. Notably,the GGUM was slightly more powerful in the fewest DF location/threshold scenarios, andthe GRM was more powerful in the most DF location/threshold scenarios. Fourth, neithermodel performed well in the complex within-item cancelling DIF scenarios. These patternsgenerally occurred in both uniform and non-uniform scenarios. The paper concludes with apresentation of recommendations, study limitations and issues for future research.
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