• Login
    View Item 
    •   Home
    • Conferences, Workshops, Lecture Series, and Symposiums
    • Phi Kappa Phi Student Research and Fine Arts Conference
    • 19th Annual Phi Kappa Phi Student Research and Fine Arts Conference (2018)
    • 19th Annual PKP Student Research and Fine Arts Conference: Oral Symposia III
    • View Item
    •   Home
    • Conferences, Workshops, Lecture Series, and Symposiums
    • Phi Kappa Phi Student Research and Fine Arts Conference
    • 19th Annual Phi Kappa Phi Student Research and Fine Arts Conference (2018)
    • 19th Annual PKP Student Research and Fine Arts Conference: Oral Symposia III
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of Scholarly CommonsCommunitiesTitleAuthorsIssue DateSubmit DateSubjectsThis CollectionTitleAuthorsIssue DateSubmit DateSubjects

    My Account

    LoginRegister

    About

    AboutCreative CommonsAugusta University LibrariesUSG Copyright Policy

    Statistics

    Display statistics

    Deep Learning for the Classification of Malicious Emails

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Authors
    Daoudi, Ahmad
    Yang, He
    Issue Date
    2018-02-12
    URI
    http://hdl.handle.net/10675.2/621722
    
    Metadata
    Show full item record
    Abstract
    Deep learning has been widely used in many real world applications, including computer vision, object and image recognition, language translation and computer-aided medical diagnosis. Compared to conventional machine learning methods, deep learning has the advantage of being able to extract the features of raw data automatically, without using hand-tuned feature extractor. In this talk, we will first presenttwo methods of constructing our datasets of malicious emails, by considering the subject line andthe context of the malicious emails, and how to convert these datasets into suitable typeof training and test datasetsfor our numerical simulations. We then present the architecture of our convolutional neural network and classification accuracy for the datasets malicious emails.
    Affiliation
    School of Computer and Cyber Sciences
    Department of Mathematics
    Description
    Presentation given at the 19th Annual Phi Kappa Phi Student Research and Fine Arts Conference
    Collections
    19th Annual PKP Student Research and Fine Arts Conference: Oral Symposia III

    entitlement

     
    DSpace software (copyright © 2002 - 2023)  DuraSpace
    Quick Guide | Contact Us
    Open Repository is a service operated by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.