• Find People
  • Campus Map
  • PiratePort
  • A-Z
    • About
    • Submit
    • Browse
    • Login
    View Item 
    •   ScholarShip Home
    • Dissertations and Theses
    • Master's Theses
    • View Item
    •   ScholarShip Home
    • Dissertations and Theses
    • Master's Theses
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of The ScholarShipCommunities & CollectionsDateAuthorsTitlesSubjectsTypeDate SubmittedThis CollectionDateAuthorsTitlesSubjectsTypeDate Submitted

    My Account

    Login

    Statistics

    View Google Analytics Statistics

    Applied Machine Learning for Cybersecurity in Spam Filtering and Malware Detection

    Thumbnail
    View/ Open
    SOKOLOV-MASTERSTHESIS-2020.pdf (2.354Mb)

    Show full item record
    Author
    Sokolov, Mark
    Abstract
    Machine learning is one of the fastest-growing fields and its application to cybersecurity is increasing. In order to protect people from malicious attacks, several machine learning algorithms have been used to predict the malicious attacks. This research emphasizes two vulnerable areas of cybersecurity that could be easily exploited. First, we show that spam filtering is a well known problem that has been addressed by many authors, yet it still has vulnerabilities. Second, with the increase of malware threats in our world, a lot of companies use AutoAI to help protect their systems. Nonetheless, AutoAI is not perfect, and data scientists can still design better models. In this thesis I show that although there are efficient mechanisms to prevent malicious attacks, there are still vulnerabilities that could be easily exploited. In the visual spoofing experiment, we show that using a classifier trained on data using Latin alphabet, to classify a message with a combination of Latin and Cyrillic letters leads to much lower classification accuracy. In Malware prediction experiment, our model has been able to predict malware attacks on Microsoft computers and got higher accuracy than any well known Auto AI.
    URI
    http://hdl.handle.net/10342/8798
    Subject
     Computer Science; LGBM; Visual spoofing; prediction 
    Date
    2020-11-17
    Citation:
    APA:
    Sokolov, Mark. (November 2020). Applied Machine Learning for Cybersecurity in Spam Filtering and Malware Detection (Master's Thesis, East Carolina University). Retrieved from the Scholarship. (http://hdl.handle.net/10342/8798.)

    Display/Hide MLA, Chicago and APA citation formats.

    MLA:
    Sokolov, Mark. Applied Machine Learning for Cybersecurity in Spam Filtering and Malware Detection. Master's Thesis. East Carolina University, November 2020. The Scholarship. http://hdl.handle.net/10342/8798. September 21, 2023.
    Chicago:
    Sokolov, Mark, “Applied Machine Learning for Cybersecurity in Spam Filtering and Malware Detection” (Master's Thesis., East Carolina University, November 2020).
    AMA:
    Sokolov, Mark. Applied Machine Learning for Cybersecurity in Spam Filtering and Malware Detection [Master's Thesis]. Greenville, NC: East Carolina University; November 2020.
    Collections
    • Computer Science
    • Master's Theses
    Publisher
    East Carolina University

    xmlui.ArtifactBrowser.ItemViewer.elsevier_entitlement

    East Carolina University has created ScholarShip, a digital archive for the scholarly output of the ECU community.

    • About
    • Contact Us
    • Send Feedback