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Deep Learning -Based Anomaly Detection System for Guarding Internet of Things Devices

Azumah, Sylvia w
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1624917874580953

Abstract Details

2021, MS, University of Cincinnati, Education, Criminal Justice, and Human Services: Information Technology.
The ever-expanding scope of the third industrial revolution spawned a dynamic digital age of computers and the world wide web (internet). The Internet of Things (IoT) is one of the latest technologies that will forever change how humans interact with information systems. These technologies involve embedding sensors and software applications into physical objects which allow them to transmit and share data with other devices over the Internet ranging from simple smart home appliance management to self-driving cars. The universal applications of IoT technology cannot be overemphasized. It is currently being utilized in remote healthcare and the telecommunications industry with a projected large-scale deployment in critical infrastructure such as power grids and water purification. As with everything else related to information systems, this presents an increased amount of vulnerabilities and security issues that could have dire consequences if left unattended. Research shows that 70% of current IoT devices are moderately easy to compromise or hack. [37]. Therefore, an efficient mechanism is needed to safeguard these devices as they are connected to the internet. This thesis introduces a novel deep learning-based anomaly detection model to predict cyberattacks on IoT devices and to identify new outliers as they occur over time. Long Short-Term Memory(LSTM) is an efficient deep learning architecture that addresses spatial and temporal information. Therefore, it could perform effectively in an anomaly detection model for IoT security. The model recorded a high detection accuracy of 98%, precision of 85%, recall of 84%, and finally an F1-score of 83% using the IoT network intrusion detection dataset. The performance of the LSTM based model approach developed in this study was analyzed and compared to the state-of-the-art deep learning-based anomaly detection for IoT devices.
Nelly Elsayed, Ph.D. (Committee Chair)
M. Murat Ozer, Ph.D. (Committee Member)
Hazem Said, Ph.D. (Committee Member)
53 p.
Information Technology
lstm; deep learning; intrusion detection

Recommended Citations

Citations

  • Azumah, S. W. (2021). Deep Learning -Based Anomaly Detection System for Guarding Internet of Things Devices [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1624917874580953

    APA Style (7th edition)

  • Azumah, Sylvia. Deep Learning -Based Anomaly Detection System for Guarding Internet of Things Devices. 2021. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1624917874580953.

    MLA Style (8th edition)

  • Azumah, Sylvia. "Deep Learning -Based Anomaly Detection System for Guarding Internet of Things Devices." Master's thesis, University of Cincinnati, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1624917874580953

    Chicago Manual of Style (17th edition)

ucin1624917874580953
99
© 2021, some rights reserved.Creative Commons License Deep Learning -Based Anomaly Detection System for Guarding Internet of Things Devices by Sylvia w Azumah is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by University of Cincinnati and OhioLINK.