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Intrusion Detection System in Smart Home Network Using Artificial Immune System and Extreme Learning Machine

Alalade, Emmanuel
http://orcid.org/0000-0002-8378-2241
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1592135245260224

Abstract Details

2020, MS, University of Cincinnati, Education, Criminal Justice, and Human Services: Information Technology.
Internet of things (IoT) applications in our daily lives has made life easier, but also comes with associated security threats. The vulnerability of the IoT system stems from the vulnerability of each connected device and transmission of threats through an interconnected home network. Smart homes are one of the applications of IoT, which is comprised of connected devices for easier interaction. An isolated IoT system with no internet connection has some level of safety from attacks because it is not exposed to the internet, although these devices have their innate vulnerabilities from the manufacturer. IoT gateways connecting IoT devices to the internet can create a backdoor into the smart home system that an attacker can exploit. Therefore, Internet-connected IoT devices have a high-security risk and one of the ways to detect an intrusion into an IoT gateway is through anomalies in the traffic passing through it. This thesis introduces early work on an intrusion detection system (IDS) by detecting anomalies in the smart home network using Extreme Learning Machine and Artificial Immune System (AIS-ELM). AIS uses the Clonal Algorithm for the optimization of the input parameters, and ELM analyzes the input parameter for better convergence in detecting anomalous activity. The larger implications of this work are the potential to apply this approach to a smart home network gateway and combine it with a push notification system that will allow the homeowner to identify any abnormalities in the smart home network and take appropriate action to mitigate threats.
Nelly Elsayed, Ph.D. (Committee Chair)
Jess Kropczynski, Ph.D. (Committee Chair)
Shane Halse, Ph.D. (Committee Member)
47 p.
Information Technology
Intrusion detection system; smart home; artificial immune system; Extreme learning machine; Artificial Immune System; Internet of Things

Recommended Citations

Citations

  • Alalade, E. (2020). Intrusion Detection System in Smart Home Network Using Artificial Immune System and Extreme Learning Machine [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1592135245260224

    APA Style (7th edition)

  • Alalade, Emmanuel. Intrusion Detection System in Smart Home Network Using Artificial Immune System and Extreme Learning Machine. 2020. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1592135245260224.

    MLA Style (8th edition)

  • Alalade, Emmanuel. "Intrusion Detection System in Smart Home Network Using Artificial Immune System and Extreme Learning Machine." Master's thesis, University of Cincinnati, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1592135245260224

    Chicago Manual of Style (17th edition)

ucin1592135245260224
1,121
© 2020, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.