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Cetin FINAL with cert.pdf (2.44 MB)

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Wireless Network Intrusion Detection and Analysis using Federated Learning

Cetin, Burak
http://rave.ohiolink.edu/etdc/view?acc_num=ysu1588778320687729

Abstract Details

2020, Master of Computing and Information Systems, Youngstown State University, Department of Computer Science and Information Systems.
Wi-Fi has become the wireless networking standard that allows short-to medium-range devices to connect without wires. For the last 20 years, the Wi-Fi technology has been so pervasive that most devices in use today are mobile and connect to the internet through Wi-Fi. Unlike wired network, a wireless network lacks a clear boundary, which leads to significant Wi-Fi network security concerns, especially because the current security measures are prone to several types of intrusion. To address this problem, machine learning and deep learning methods have been successfully developed to identify network attacks. However, collecting data to develop models is expensive and raises privacy concerns. The goal of this thesis is to evaluate a federated learning approach that would alleviate such privacy concerns. This work on intrusion detection is performed in a simulated environment. During the work, different experiments have concluded to define points that can affect the accuracy of a model to allow edge devices to collaboratively update global anomaly detection models using a privacy-aware approach. Three comparison tests were done in order to find the optimal results; different training rates, different training methods, different parameters. Using different combinations of 5 parameters - training algorithms, number of epochs, devices per round, round numbers and size of the sample set-, these tests with the AWID intrusion detection data set, show that our federated approach is effective in terms of classification accuracy (with an accuracy range of 88-95%), computation cost, as well as communication cost. In our study, the best case had the most rounds, epoch and the devices per round compared to the others.
Alina Lazar, PhD (Advisor)
Feng Yu, PhD (Committee Member)
John Sullins, PhD (Committee Member)
40 p.
Artificial Intelligence; Computer Science; Information Science; Information Systems
Federated Learning; Wireless Intrusion Detection; Anomaly Detection; Machine Learning

Recommended Citations

Citations

  • Cetin, B. (2020). Wireless Network Intrusion Detection and Analysis using Federated Learning [Master's thesis, Youngstown State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1588778320687729

    APA Style (7th edition)

  • Cetin, Burak. Wireless Network Intrusion Detection and Analysis using Federated Learning. 2020. Youngstown State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ysu1588778320687729.

    MLA Style (8th edition)

  • Cetin, Burak. "Wireless Network Intrusion Detection and Analysis using Federated Learning." Master's thesis, Youngstown State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1588778320687729

    Chicago Manual of Style (17th edition)

ysu1588778320687729
1,774
© 2020, all rights reserved.
This open access ETD is published by Youngstown State University and OhioLINK.