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Real-time intrusion detection system in IOT medical devices

Phang, Joshua Jen Hoe (2022) Real-time intrusion detection system in IOT medical devices. Final Year Project, UTAR.

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    The wide adoption of the Internet of Things (IoT) in the current digital world is gradually increasing with time, focusing on the various benefits and huge convenience IoT can bring about to the way people live. However, new technological advancements will always be introduced to potentially new, unknown security threats and vulnerabilities, hence a real-time intrusion detection system is implemented in this project. This research-based cybersecurity project highlights the importance of an intrusion detection system in improving the security level of the IoT medical devices. The design of the real-time IDS revolves around setting up simple IoT devices resembling IoT medical devices to form an IoT network, performing attacks on the network, capturing network packets in real-time, and classifying network data with a deep learning framework to help in identifying modern intrusions and network traffic anomalies. Some network attacks are performed within the network and the packet data are captured at the same time. Generative adversarial network will be used as the deep-learning-based generative model for anomalous intrusion detection purposes. The model itself will be trained and tested with a network intrusion dataset for benchmarking the model performance. In the context of real-time IDS, this project aims to improve the security aspects of the IoT medical devices, and possibly spark the importance of security technologies like IDS in the IoT industry.

    Item Type: Final Year Project / Dissertation / Thesis (Final Year Project)
    Subjects: A General Works > AC Collections. Series. Collected works
    Q Science > Q Science (General)
    S Agriculture > SB Plant culture
    Divisions: Faculty of Information and Communication Technology > Bachelor of Information Technology (Honours) Communications and Networking
    Depositing User: ML Main Library
    Date Deposited: 15 Jan 2023 21:41
    Last Modified: 15 Jan 2023 21:41
    URI: http://eprints.utar.edu.my/id/eprint/4682

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