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  • Establishing a comprehensive census of undergraduate economics curricula: Foundational and special requirements for major programs in the U.S.

    Student Work thumbnail: Establishing a comprehensive census of undergraduate economics curricula: Foundational and special requirements for major programs in the U.S.
    Type:
    Student Work
    Description/Abstract:
    Abstract This study is the first of a series of studies, collectively embodying a multiphase mixed methods design. The overall objective of these studies is to explore and address a variety of issues and features of the discipline of economics, particularly as they relate to and represent past present and future factors of globalization, education, citizenship, and society. This is done by collecting and analyzing data on numerous aspects of the undergraduate economics curriculum, economics as a discipline, and economics as applied in the real world. The overall purpose of these studies is to inform ongoing debates concerning the future of the discipline of economics and how it is taught, by examining and creating paradigms and methods that may be of aide. Additionally these studies collectively aim to outline, and in small ways develop, potential technological and organizational solutions for detailed longitudinal curriculum tracking. The frameworks employed and developed in these studies may eventually be scaled and adapted for all sorts of curricula. Ideally, the completion of this study’s overall objective yields practical insights and tools that empower faculty and departments, in economics and eventually in general, to better understand and design their own curriculum. This immediate study fills gaps in and updates data on the curriculum of undergraduate economics majors in U.S. institutions, while also establishing a baseline data set for future studies to build on. A qualitative census methodology is adapted and employed to explore how various institutional and program factors relate to certain types of major program requirements. Descriptive statistics are used for analysis, primarily to allow for comparisons to previous studies. In sum, the purpose of the data collected and analyzed in this census is to give a glimpse into the current state of the undergraduate economics curriculum in the U.S., and to inform the qualitative, quantitative, and transformative studies that are to follow in this multiphase series.
    Creator/Author:
    Turner, Grant
    Submitter:
    Grant Turner
    Date Uploaded:
    06/18/2020
    Date Modified:
    06/18/2020
    Date Created:
    2018-05-19
    License:
    Attribution-NonCommercial-ShareAlike 4.0 International

  • Promotion of food allergy by the unfolded protein response (preprint)

    Document thumbnail: Promotion of food allergy by the unfolded protein response (preprint)
    Type:
    Document
    Description/Abstract:
    This is a preprint of a to be submitted paper that demonstrates that: (1) many important food allergens (eggs, milk, peanuts, tree nuts) induce the unfolded protein response (UPR) in intestinal epithelial cells; (2) induction of the UPR, in turn, stimulates the expression of pro-Th2 cytokines (IL-25, IL-33, TSLP) that are required for the induction of food allergy by these cytokines; (3) egg allergy is suppressed in mouse models by the UPR inhibitor, metformin (a drug widely used to treat diabetes mellitus); and (4) metformin appears to have a protective effects in humans who have alpha-gal syndrome, which is a form of food allergy.
    Creator/Author:
    Finkelman, Fred
    Submitter:
    Fred Finkelman
    Date Uploaded:
    06/02/2020
    Date Modified:
    06/02/2020
    Date Created:
    2020-06-02
    License:
    Open Data Commons Attribution License (ODC-By)

  • IoT Emergency Detection Devices

    Article thumbnail: IoT Emergency Detection Devices
    Type:
    Article
    Description/Abstract:
    In a world where technology continues to vastly grow and improve, IoT devices have increasingly become more and more a part of people’s everyday lives. Although that is the case there is a need to understand how to better use these devices for threat detection. This paper presents early work to understand gaps in this regard using a review of previously used techniques to identify known threats to households. Through the use of smart home device clusters we seek to effectively reduce the amount of false alarms and create a more reliable resource for home residents.
    Creator/Author:
    Tresenwriter, Isaac
    Submitter:
    Jess Kropczynski
    Date Uploaded:
    05/15/2020
    Date Modified:
    05/15/2020
    Date Created:
    2020-04-14
    License:
    All rights reserved

  • Securing A Small Office Home Office Network: Integrating A Supervised Neural Network Algorithm

    Article thumbnail: Securing A Small Office Home Office Network: Integrating A Supervised Neural Network Algorithm
    Type:
    Article
    Description/Abstract:
    Small office home office networks have become a target for many threat actors, hackers and cyber attackers and hence there is an urgent need to secure the network from such attackers. Most small office home office network users do not see the need to provide enough security to their networks because they assume no one is going to hack them forgetting that the biggest threat of our small home networks today comes from the outside. The challenge of misconfiguration of routers, firewalls and default configurations in our small home networks renders the network vulnerable to attacks such as DDos , phishing attacks , virus and other network attacks hence the need to implement a detection algorithm to help identify flaws in the pattern of the small office network. It turns out that about 75% of existing approaches focused on intrusion detection in 802.11 wireless networks of a SOHO and not the entire network. These approaches do not efficiently secure the network entirely leaving the rest prone to attacks can occur with or without the internet. This paper proposes to add another layer of security to the other preventive measures in a SOHO network by designing, implementing and testing a supervised neural network algorithm to identify attacks on the small home network and also to send a notification to users to keep them informed of the activities on their network. The supervised neural network algorithm will have a dataset representing both attacks and non-attacks which will be used in the training phase. The system should be able to detect and identify the various attacks and anomalies when they occur on the network and help keep the users informed.
    Creator/Author:
    Azumah, Sylvia Worlali and Li, Chengcheng
    Submitter:
    Jess Kropczynski
    Date Uploaded:
    05/15/2020
    Date Modified:
    05/15/2020
    Date Created:
    2020-04-14
    License:
    All rights reserved

  • Implementation of a hybrid machine learning technique for network intrusion detection

    Article thumbnail: Implementation of a hybrid machine learning technique for network intrusion detection
    Type:
    Article
    Description/Abstract:
    The current rapid growth in the computer and internet development has ushered in numerous cybersecurity challenges which are constantly evolving with time. The current cybersecurity solutions are no longer optimal in tackling these emerging cyber threats and attacks. This paper proposes the creation of a cybersecurity dataset to be used for a hybrid machine learning (ML) approach of supervised and unsupervised learning for an effective intrusion detection system. The proposed model entails a five-stage process which starts at the setup of a simulated network environment of network attacks to generate a dataset which feeds into the data normalization stage and then to data dimension reduction stage using the principal component analysis as a feature extraction method after which the data of reduced dimension is clustered using the k-Means method to bring about a new data set with fewer features. This new dataset is afterward classified using the enhanced support vector machine (ESVM). The proposed model is expected to provide a high-quality dataset and an efficient intrusion detection system in terms of intrusion detection accuracy of 99.5%, short train time of 3seconds and a low false-positive rate of 0.4%.
    Creator/Author:
    Eichie, Maxwell
    Submitter:
    Jess Kropczynski
    Date Uploaded:
    05/15/2020
    Date Modified:
    05/15/2020
    Date Created:
    2020-04-14
    License:
    All rights reserved

  • Crime Hot Spots: Proposing a new model for appropriate identification of crime hot spots

    Article thumbnail: Crime Hot Spots: Proposing a new model for appropriate identification of crime hot spots
    Type:
    Article
    Description/Abstract:
    This research focuses on two fundamental aspects of hot spot policing that have been widely neglected by previous scholarly research. These aspects include the adequate concentration of crime at a smaller geographical unit to be considered a crime hot spot, and the cost-benefit implication of focusing limited police resources on such a smaller place in an effort to prevent criminal activities. Substantial limitations in call-t- service data from police departments raise concern on the purported concentration of crime at places that warrant such strategy in the first place. We will examine data from the Cincinnati Police Department and propose guidelines on adopting a threshold when designating places as crime hot spots, using time and cost-benefit analysis as key determinants.
    Creator/Author:
    Hussein, Abdul Aziz and Ozer, Murat
    Submitter:
    Jess Kropczynski
    Date Uploaded:
    05/15/2020
    Date Modified:
    05/15/2020
    Date Created:
    2020-04-14
    License:
    All rights reserved

  • Scraping The Deep Web: A 3-Dimensional Framework For Cyber-Threat Intelligence

    Article thumbnail: Scraping The Deep Web: A 3-Dimensional Framework For Cyber-Threat Intelligence
    Type:
    Article
    Description/Abstract:
    Cyberspace is one of the most complex systems ever built by humans. The utilization of cybertechnology resources are used ubiquitously by many, but sparsely understood by the majority of the users. In the past, cyberattacks were usually orchestrated in a random pattern of attack to lure unsuspecting targets. However, the cyber virtual environment is an ecosystem that provided a platform for an organized and sophisticated approach to launch an attack against a specific target group or organization by nefarious actors. In 2019, the average cost of cyber-attack in the US was about $1.6 million. This paper proposes a 3D framework to signal new threat alert before the actual occurrence of the threat on the surface web to alert cybersecurity experts and law enforcement agencies in preventive measures or means of mitigating the severity of damage caused by cyberattacks. The methodology combines information extracted from the deep web through a smart web crawler with socio-personal and technical indicators from twitter which is mapped with OTX (Open Threat Exchange). The OTX is an open-source cyber threat platform managed by security experts. The OTX endpoint security tool(OTX python SDK) will be used to identify a new type of cyber threats. The effectiveness of the framework will be tested using the machine learning algorithm precision-recall rate.
    Creator/Author:
    Adewale, Adewopo Victor and Gonen, Bilal
    Submitter:
    Jess Kropczynski
    Date Uploaded:
    05/15/2020
    Date Modified:
    05/15/2020
    Date Created:
    2020-04-14
    License:
    All rights reserved

  • Relative Importance of Blockchain in Health Care: Enhancing data sharing and mitigating cyber security crimes

    Article thumbnail: Relative Importance of Blockchain in Health Care: Enhancing data sharing and mitigating cyber security crimes
    Type:
    Article
    Description/Abstract:
    This paper looks at the opportunities and challenges of implementing blockchain technology across the medical sector and provides a clear view which can enable blockchain for more extents. After a notable research on underlying blockchain technology which offers distributed governance, immutable audit trail, provenance of data, robustness and privacy, we contrasted blockchain innovations and identified prominent applications of it in historically decentralized healthcare sectors. As the healthcare industry faces many challenges like unauthorised data sharing, lack of data transparency, ransomware, data breaches and cyber crimes, blockchain is one of the best ways to enhance data sharing and to mitigate prominent cyber crimes. By proper designing of a decentralized and immutable blockchain network where the data is dispersed among credentialed social insurance experts guarantees that cybercriminals cannot touch single patient’s confidential data, which facilitates encryption or cryptography of personal data where no patient’s emergency data is at extreme hazard. Blockchain trust-worthy cloud is one of the most powerful and secure ways of storing high confidential data. After analysing Blockchain implementations and identifying its potential in healthcare, we conclude with several promising directions for future research.
    Creator/Author:
    Ponnakanti, Hari Priya; Ozer, Murat, and Gonen, Bilal
    Submitter:
    Jess Kropczynski
    Date Uploaded:
    05/15/2020
    Date Modified:
    05/15/2020
    Date Created:
    2020-04-14
    License:
    All rights reserved

  • A methodology for cyberthreat ranking

    Article thumbnail: A methodology for cyberthreat ranking
    Type:
    Article
    Description/Abstract:
    National Institute of Standards and Technology (NIST) recommends that organizations perform cyber risk assessments regularly to identify security vulnerabilities and to control levels of exposure to threats. We discuss a method to customize the ranking of cyber threats based on the organization’s maturity level of implementing NIST controls and we use FAIR model’s LEF component as a measure of the severity of cyber threats. The methodology integrates NIST maturity levels to calculate the resistance strength factor and produce the LEF values for each threat. The LEF value is then used to represent the severity level of the threat to the specific organization. This hybrid risk assessment approach will help stakeholders make data-informed decisions on improving security measures and provide accurate values that represent the current security state of their organization.
    Creator/Author:
    Bakare, Adeyinka and Said, Hazem
    Submitter:
    Jess Kropczynski
    Date Uploaded:
    05/15/2020
    Date Modified:
    05/15/2020
    Date Created:
    2020-04-14
    License:
    All rights reserved

  • Application of Autoencoder Duets in Anomaly and Intrusion Detection

    Article thumbnail: Application of Autoencoder Duets in Anomaly and Intrusion Detection
    Type:
    Article
    Description/Abstract:
    Signature-based intrusion detection methods report high accuracy with a low false alarm rate. However, they do not perform well when faced with new or emerging threats. This work focuses on anomaly-based data driven methods to identify potential zero-day-attacks using a specific class of neural networks known as the autoencoder.
    Creator/Author:
    Li, Chengcheng; Lee, Kijung; Mathur, Nitin, and Gonen, Bilal
    Submitter:
    Jess Kropczynski
    Date Uploaded:
    05/15/2020
    Date Modified:
    05/15/2020
    Date Created:
    2020-04-14
    License:
    All rights reserved