• Login
    View Item 
    •   Home
    • Doctoral Degree Granting Institutions
    • SUNY Polytechnic Institute
    • SUNY Polytechnic Institute Master's Theses and Projects
    • SUNY Polytechnic Institute College of Engineering
    • View Item
    •   Home
    • Doctoral Degree Granting Institutions
    • SUNY Polytechnic Institute
    • SUNY Polytechnic Institute Master's Theses and Projects
    • SUNY Polytechnic Institute College of Engineering
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of SUNY Open Access RepositoryCommunitiesPublication DateAuthorsTitlesSubjectsDepartmentThis CollectionPublication DateAuthorsTitlesSubjectsDepartmentAuthor ProfilesView

    My Account

    LoginRegister

    Campus Communities in SOAR

    Alfred State CollegeBrockportBroomeCantonDownstateDutchessEmpireFarmingdaleFinger LakesFredoniaHerkimerMaritimeNew PaltzNiagaraOld WestburyOneontaOnondagaOptometryOswegoPlattsburghPurchase CollegePolytechnic InstituteSUNY Office of Workforce Development and Upward MobilitySUNY PressUpstate Medical

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    An Empirical Wi-Fi Intrusion Detection System

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    Basnet_Diwash_MS Project.pdf
    Size:
    1.007Mb
    Format:
    PDF
    Download
    Average rating
     
       votes
    Cast your vote
    You can rate an item by clicking the amount of stars they wish to award to this item. When enough users have cast their vote on this item, the average rating will also be shown.
    Star rating
     
    Your vote was cast
    Thank you for your feedback
    Author
    Basnet, Diwash Bikram
    Kholidy, Hisham A.; Advisor
    Keyword
    wireless networks
    cybersecurity
    hackers
    intrusion detection systems
    machine learning
    neural networks
    Date Published
    2020-05
    
    Metadata
    Show full item record
    URI
    http://hdl.handle.net/20.500.12648/1603
    Abstract
    Today, the wireless network devices are growing rapidly, and it is of utmost importance for securing those devices. Attackers or hackers use new methods and techniques to trick the system and steal the most important data. Intrusion Detection Systems detect the attacks by inspecting the network traffics or logs. The work demonstrated the effectiveness of detecting the attacks using machine learning techniques on the AWID dataset, which is produced from real wireless network logging. The author of the AWID dataset may have used several supervised learning models to successfully detect the intrusions. In this paper, we propose a newer approach for intrusion detection model based on dense neural networks, and long short-term memory networks (LSTM) and evaluate the model against the AWID-CLS-R subset. To get the best results from the model, we applied feature selection by replacing the unknown data with the value of “none”, getting rid of all repeated values, and kept only the important features. We did preprocess and feature scaling of both training and testing dataset, additional we also change the 2-dimensional to the 3- dimensional array because LSTM takes an input of 3-dimensional array, and later we used flatten layers to change into a 2-dimensional array for output. A comprehensive evaluation of DNN and LSTM networks are used to classify and predict the attacks and compute the precision, recall, and F1 score. We perform binary classification and multiclass classification on the dataset using neural networks and achieve accuracy ranging from 86.70 % to 96.01%.
    Citation
    Basnet, D. B., & Kholidy, H. A. (2020). An Empirical Wi-Fi Intrusion Detection System: A Master’s Project Presented to the Department of Network and Computer Security in Partial Fulfillment of the Requirements for the Master of Science Degree. Department of Network and Computer Security, College of Engineering, SUNY Polytechnic Institute.
    Collections
    SUNY Polytechnic Institute College of Engineering

    entitlement

     

    DSpace software (copyright © 2002 - 2025)  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.