Buscar

Resultados de la búsqueda

  • 062_DC_.tif

    Image thumbnail: 062_DC_.tif
    Type:
    Image
    Descripción/Resumen:
    Sketch of fire escape building for victory city cafeteria F-Family Cart Eating Area. Two pieces of paper taped onto one larger piece of paper
    Creador/Autor:
    Center, Simpson
    Peticionario:
    Simpson Center
    Fecha modificada:
    05/19/2020
    Fecha de creacion:
    1988/04/28
    Licencia:
    All rights reserved

  • 040_DC.tif

    Image thumbnail: 040_DC.tif
    Type:
    Image
    Descripción/Resumen:
    Illustration of ground view of cafeteria (loose sketch)
    Creador/Autor:
    Center, Simpson
    Peticionario:
    Simpson Center
    Fecha modificada:
    05/19/2020
    Licencia:
    All rights reserved

  • 015_DC.tif

    Image thumbnail: 015_DC.tif
    Type:
    Image
    Descripción/Resumen:
    Cafeteria sketch on the back of apartment building statistics
    Creador/Autor:
    Center, Simpson
    Peticionario:
    Simpson Center
    Fecha modificada:
    05/19/2020
    Licencia:
    All rights reserved

  • 007_DC.tif

    Image thumbnail: 007_DC.tif
    Type:
    Image
    Descripción/Resumen:
    Cafeteria sketch on the back of democrat flyer
    Creador/Autor:
    Center, Simpson
    Peticionario:
    Simpson Center
    Fecha modificada:
    05/19/2020
    Licencia:
    All rights reserved

  • 2020 BUV (basic utility vehicle): steering and suspension

    Student Work thumbnail: 2020 BUV (basic utility vehicle): steering and suspension
    Type:
    Student Work
    Descripción/Resumen:
    Senior design capstone report
    Creador/Autor:
    Lamascid, Gustavo
    Peticionario:
    CEAS Library Staff
    Fecha modificada:
    05/19/2020
    Fecha modificada:
    05/19/2020
    Licencia:
    Attribution-NonCommercial-NoDerivs 4.0 International

  • 46mm end cap: cost savings

    Student Work thumbnail: 46mm end cap: cost savings
    Type:
    Student Work
    Descripción/Resumen:
    Senior design capstone report
    Creador/Autor:
    McCartney, Dan
    Peticionario:
    CEAS Library Staff
    Fecha modificada:
    05/18/2020
    Fecha modificada:
    05/18/2020
    Licencia:
    Attribution-NonCommercial-NoDerivs 4.0 International

  • IoT Emergency Detection Devices

    Article thumbnail: IoT Emergency Detection Devices
    Type:
    Article
    Descripción/Resumen:
    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.
    Creador/Autor:
    Tresenwriter, Isaac
    Peticionario:
    Jess Kropczynski
    Fecha modificada:
    05/15/2020
    Fecha modificada:
    05/15/2020
    Fecha de creacion:
    2020-04-14
    Licencia:
    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
    Descripción/Resumen:
    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.
    Creador/Autor:
    Azumah, Sylvia Worlali and Li, Chengcheng
    Peticionario:
    Jess Kropczynski
    Fecha modificada:
    05/15/2020
    Fecha modificada:
    05/15/2020
    Fecha de creacion:
    2020-04-14
    Licencia:
    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
    Descripción/Resumen:
    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%.
    Creador/Autor:
    Eichie, Maxwell
    Peticionario:
    Jess Kropczynski
    Fecha modificada:
    05/15/2020
    Fecha modificada:
    05/15/2020
    Fecha de creacion:
    2020-04-14
    Licencia:
    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
    Descripción/Resumen:
    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.
    Creador/Autor:
    Hussein, Abdul Aziz and Ozer, Murat
    Peticionario:
    Jess Kropczynski
    Fecha modificada:
    05/15/2020
    Fecha modificada:
    05/15/2020
    Fecha de creacion:
    2020-04-14
    Licencia:
    All rights reserved